Information recommendation apparatus and information recommendation system

ABSTRACT

An information recommendation apparatus has
         recommendation means of selecting and recommending contents coincident with or similar to conditions input by condition input means of inputting the conditions represented by predetermined items and attribute values corresponding thereto designated through the terminal of a user via the Internet, from among contents formed of plural pieces of data having plural items and attribute values corresponding thereto and stored in a content database in which the contents are registered by registration means, wherein   the recommended contents are output to the terminal by output means via the Internet.

This application is a Divisional of U.S. patent application Ser. No.09/851,791 filed May 9, 2001 which claims priority of IP 2000-145169filed May 17, 2000 the entire disclosure of which is incorporated hereinby reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an information recommendationapparatus, an information recommendation system and a program forproviding information requested by a user from among abundantinformation through the Internet or the like.

2. Related Art of the Invention

Recently, information communication apparatuses, such as personalcomputers and portable information terminals, became widespread rapidly.Hence, when it is desired to obtain some new information, an action ofgaining access to an information server and extracting desiredinformation by using a WWW browser installed in such an informationcommunication apparatus has been carried out daily. For example, when itis difficult to plan a dinner meal, a person gains access to a home pagecarrying abundant recipes for dishes and inputs desired conditions, suchas ingredients, cooking time and calories, whereby he or she can obtainrecipes conforming to the conditions.

On home pages carrying such recipes for dishes, recipes for verypresentable dishes, recipes for nutritionally balanced dishes, etc. havebeen registered beforehand by cooking specialists, dieticians, etc. andthese are searched for by users.

However, to realize information service using such an informationserver, abundant contents (recipes for dishes in this example), and thequality of the service greatly depends on the completeness of thecontents. Hence, how to prepare abundant contents is a big problem.

In other words, in order to accomplish information service using aninformation server, how to prepare abundant contents is a problem.

In addition, since maintenance cost for constructing and maintainingsuch a large database is considerably high, it is necessary to have anarrangement for recovering the maintenance cost.

In other words, preparing an arrangement for recovering the maintenancecost for constructing and maintaining such a large database is aproblem.

Furthermore, in a conventional information recommendation method, a userhimself or herself is required to input desired information conditions.In this case, the user himself or herself is required to clarifyinformation he or she desires. Hence, it is difficult to find outinformation that is exactly suited for the user but unnoticed.

In other words, the difficulty in finding out information that isexactly suited for the user but unnoticed is a problem in theconventional information recommendation method.

Furthermore, the recipes provided by the conventional method are idealrecipes prepared beforehand by cooking specialists, and the recipes arefor gorgeous menus for guests rather than daily menus for family. Hence,the recipes are not suited for actual daily menus for family.

In other words, the recipes provided by the conventional method are notsuited for actual daily menus for family, resulting in a problem.

Furthermore, the recipes prepared by the cooking specialists describedishes that are excellent in nutritional balance and ideal in theory.However, the recipes are void of viewpoints obtained by the result ofactually using the recipes, such as at what time a person wishes to eata dish cooked according to one of the recipes and how the person feltafter eating the dish. Hence, there is a danger that the recipes maybecome data not suited for actual situations.

In other words, the recipes provided by the conventional method are voidof viewpoints obtained by the result of actually using the recipes, andthere is a danger that the recipes may become data not suited for actualsituations, resulting in a problem.

SUMMARY OF THE INVENTION

In consideration of the above-mentioned problems and in order toaccomplish information service using an information server, the presentinvention is intended to provide an information recommendationapparatus, an information recommendation system and a program capable ofpreparing abundant contents.

Furthermore, in consideration of the above-mentioned problems, thepresent invention is intended to provide an information recommendationapparatus, an information recommendation system and a program capable ofrecovering maintenance cost for constructing and maintaining a largedatabase.

Furthermore, in consideration of the above-mentioned problems, thepresent invention is intended to provide an information recommendationapparatus, an information recommendation system and a program capable ofeasily finding out information that is exactly suited for a user butunnoticed.

Furthermore, in consideration of the above-mentioned problems, thepresent invention is intended to provide an information recommendationapparatus, an information recommendation system and a program capable ofproviding recipes suited for actual daily menus for family.

Furthermore, in consideration of the above-mentioned problems, thepresent invention is intended to provide an information recommendationapparatus, an information recommendation system and a program capable ofproviding recipes not void of viewpoints obtained by the result ofactually using the recipes and suited for actual situations.

A first exemplary embodiment of the present invention relates to aninformation recommendation apparatus comprising:

recommendation means of selecting and recommending contents coincidentwith or similar to conditions input by condition input means ofinputting said conditions represented by predetermined items andattribute values corresponding thereto designated through the terminalof a user via the Internet, from among contents formed of plural piecesof data having plural items and attribute values corresponding theretoand stored in a content database in which said contents are registeredby registration means, wherein

said recommended contents are output to said terminal by output meansvia said Internet.

A second exemplary embodiment of the present invention relates to aninformation recommendation apparatus comprising:

recommendation means of selecting and recommending contents coincidentwith or similar to conditions input by condition input means ofinputting said conditions represented by predetermined items andattribute values corresponding thereto, from among contents formed ofplural pieces of data having plural items and attribute valuescorresponding thereto and stored in a content database in which saidcontents are registered by registration means, wherein

said recommended contents are output by output means, and

said items are subjective items resulting from human decisions andimpressions, and/or objective items free from human decisions andimpressions.

A third exemplary embodiment of the present invention relates to aninformation recommendation apparatus comprising:

recommendation means of selecting and recommending contents coincidentwith or similar to conditions input by condition input means ofinputting said conditions represented by predetermined items andattribute values corresponding thereto, from among contents formed ofplural pieces of data having plural items and attribute valuescorresponding thereto and stored in a content database in which saidcontents are registered by registration means, wherein

said recommended contents are output by output means, and

a scoring method for scoring points to each of said items depending onthe degree of similarity is predetermined in order to judge thesimilarity between said conditions to be input and each of saidcontents.

A fourth exemplary embodiment of the present invention relates to aninformation recommendation apparatus comprising:

recommendation means of selecting and recommending contents coincidentwith or similar to conditions input by condition input means ofinputting said conditions represented by predetermined items andattribute values corresponding thereto, from among contents formed ofplural pieces of data having plural items and attribute valuescorresponding thereto and stored in a content database in which saidcontents are registered by registration means, wherein

said recommended contents are output by output means, and

a thesaurus is used to judge the similarity between said conditions tobe input and each of said contents in the case when said attributevalues are represented by words.

A fifth exemplary embodiment of the present invention relates to aninformation recommendation apparatus comprising:

recommendation means of selecting and recommending contents coincidentwith or similar to conditions input by condition input means ofinputting said conditions represented by predetermined items andattribute values corresponding thereto, from among contents formed ofplural pieces of data having plural items and attribute valuescorresponding thereto and stored in a content database in which saidcontents are registered by registration means, wherein

said recommended contents are output by output means, and

the number of times a user attempting to receive recommendation receivesrecommendation or the content of the recommendation is determineddepending on the number of times said user carried out registration byusing said content registration means.

A sixth exemplary embodiment of the present invention relates to aninformation recommendation apparatus according to the fifth exemplaryembodiment, wherein the number of registration times of said user isdetermined (a) by checking the access history of said user with respectto registration or (b) by assigning the user ID of the registrant tosaid content and by using said user ID.

A seventh exemplary embodiment of the present invention relates to is aninformation recommendation apparatus comprising:

recommendation means of selecting and recommending contents coincidentwith or similar to conditions input by condition input means ofinputting said conditions represented by predetermined items andattribute values corresponding thereto, from among contents formed ofplural pieces of data having plural items and attribute valuescorresponding thereto and stored in a content database in which saidcontents are registered by registration means, wherein

said recommended contents are output by output means, and

said conditions input to said condition input means are conditionsextracted by condition extraction means of automatically extracting saidconditions.

An eighth exemplary embodiment of the present invention relates to aninformation recommendation apparatus according to the seventh exemplaryembodiment, wherein the conditions to be input to said condition inputmeans are those extracted on the basis of contents registered in thepast by a user who will receive recommendation.

A ninth exemplary embodiment of the present invention relates to aninformation recommendation apparatus according to the seventh exemplaryembodiment, wherein the characteristic of each item of said user isobtained by calculation each time said user registers said data.

A tenth exemplary embodiment of the present invention relates to aninformation recommendation apparatus according to the seventh exemplaryembodiment, wherein the conditions to be input to said condition inputmeans are extracted on the basis of contents recommended in the past toa user who is attempting to receive recommendation or on the basis ofcontents recommended to and specified by said user.

An eleventh exemplary embodiment of the present invention relates to aninformation recommendation apparatus according to the tenth exemplaryembodiment, wherein the characteristic of each item of said user isobtained by calculation by characteristic calculation means each timesaid user receives recommendation or each time said user receivesrecommendation and specifies the contents.

A twelfth exemplary embodiment of the present invention relates to aninformation recommendation apparatus according to the eighth or tenthexemplary embodiments, wherein, when said conditions are extracted fromsaid contents, said conditions having tendencies opposite to those ofsaid contents are extracted.

A thirteenth exemplary embodiment of the present invention relates to aninformation recommendation apparatus according to the seventh exemplaryembodiment, wherein said condition input means inputs said externallyinput conditions and said automatically extracted conditions, and

said recommendation means selects contents coincident with or similar tosaid automatically extracted conditions from only said contentsconforming to said externally input conditions and recommend saidselected contents.

A fourteenth exemplary embodiment of the present invention relates to aninformation recommendation apparatus comprising:

recommendation means of selecting and recommending contents coincidentwith or similar to conditions input by condition input means ofinputting said conditions represented by predetermined items andattribute values corresponding thereto, from among contents formed ofplural pieces of data having plural items and attribute valuescorresponding thereto and stored in a content database in which saidcontents are registered by registration means, wherein

said recommended contents are output by output means,

on the basis of contents registered in the past by a user attempting toreceive recommendation, contents recommended to said user or contentsrecommended to and specified by said user, characteristic informationcalculation means of obtaining characteristic information by calculationfor each of said items and storing said characteristic informationobtains said characteristic information by calculation and stores saidcharacteristic information, and

in the case of recommendation to a specific user, said recommendationmeans specifies other users whose characteristic information iscoincident or similar to said characteristic information of saidspecific user by using said stored characteristic information, andselects and recommends contents registered in the past by said otherusers, contents recommended to said other users or contents recommendedto and specified by said other users.

A fifteenth exemplary embodiment of the present invention relates to aninformation recommendation apparatus comprising:

recommendation means of selecting and recommending recipes coincidentwith or similar to conditions input by condition input means ofinputting said conditions represented by predetermined items andattribute values corresponding thereto from a content database, that is,from among said recipes formed of plural pieces of data having pluralitems and attribute values corresponding thereto and stored in saidcontent database in which said recipes are registered by registrationmeans, wherein

said recommended contents are output by output means,

said content database is classified into recipe groups for dishes takenfor a meal, and

when said conditions are input, said recommendation means determines arecipe coincident with or most similar to said conditions, and selectsand determines all or part of recipes other than said determined recipe.

A sixteenth exemplary embodiment of the present invention relates to aninformation recommendation apparatus comprising:

recommendation means of selecting and recommending contents coincidentwith or similar to conditions input by condition input means ofinputting said conditions represented by predetermined items andattribute values corresponding thereto, from among contents formed ofplural pieces of data having plural items and attribute valuescorresponding thereto and stored in a content database in which saidcontents are registered by registration means, wherein

said recommended contents are output by output means,

on the basis of contents registered in the past by a user attempting toreceive recommendation, contents recommended to said user or contentsrecommended to and specified by said user, characteristic informationcalculation means of obtaining characteristic information by calculationfor each of said items and storing said characteristic informationobtains said characteristic information by calculation and stores saidcharacteristic information,

while various characteristics regarding said user have been input, typeinformation calculation means of using said characteristic informationobtained for each of said items by calculation for each characteristicas said type information, and

type judgment means judges the type of said user attempting to receiverecommendation by comparing said characteristic information of said userattempting to receive recommendation with said type information.

A seventeenth exemplary embodiment of the present invention relates toan information recommendation apparatus comprising:

recommendation means of selecting and recommending contents coincidentwith or similar to conditions input by condition input means ofinputting said conditions represented by predetermined items andattribute values corresponding thereto, from among contents formed ofplural pieces of data having plural items and attribute valuescorresponding thereto and stored in a content database in which saidcontents are registered by registration means, wherein

said recommended contents are output by output means, and

among advertisements stored in an advertisement database for storingplural advertisements having related information having the sameconfiguration as those of said items and said attribute values in saidcontents, advertisements coincident with or similar to said inputconditions are specified by advertisement specifying means.

An eighteenth exemplary embodiment of the present invention relates toan information recommendation apparatus comprising:

recommendation means of selecting and recommending contents coincidentwith or similar to conditions input by condition input means ofinputting said conditions represented by predetermined items andattribute values corresponding thereto, from among contents formed ofplural pieces of data having plural items and attribute valuescorresponding thereto and stored in a content database in which saidcontents are registered by registration means, wherein

said recommended contents are output by output means, and

among advertisements stored in an advertisement database for storingplural advertisements having related information having the sameconfiguration as those of said item and said attribute value in saidcontents, on the basis of contents registered in the past by a userattempting to receive recommendation, contents recommended to said useror contents recommended to and specified by said user, saidadvertisement, which is similar to the characteristic information ofeach item for said user and is obtained by calculation and stored, isspecified by advertisement specifying means.

A nineteenth exemplary embodiment of the present invention relates to aninformation recommendation apparatus according to the seventeenth oreighteenth exemplary embodiments, wherein

the number of times said advertisement is specified is counted by anadvertisement counter, and

an advertisement rate is charged depending on the count value of saidadvertisement counter.

A twentieth exemplary embodiment of the present invention relates to aninformation recommendation apparatus according to one of the first tosixth and thirteenth to nineteenth exemplary embodiments, wherein saidcondition input means inputs said conditions by using a one-dimensionalor two-dimensional interface.

A twenty-first exemplary embodiment of the present invention relates toan information recommendation apparatus according to one of the first tonineteenth exemplary embodiments, wherein said recommendation means doesnot recommend said contents recommended to a user in a predeterminedperiod in the past to said user again.

A twenty-second exemplary embodiment of the present invention relates toan information recommendation apparatus according to one of the first,second, and fifth to nineteenth exemplary embodiments, wherein saidrecommendation means selects and recommends contents including acharacter string coincident with the character string included in saidinput conditions from said content database.

A twenty-third exemplary embodiment of the present invention relates toan information recommendation apparatus comprising:

from among contents formed of plural pieces of data having plural itemsand attribute values corresponding thereto and stored in a contentdatabase in which said contents are registered by registration means,

recommendation means, in the case when recommendation is performed tosaid user having an input user ID, of specifying other users whosecharacteristic information is coincident or similar to saidcharacteristic information of said user having said user ID by usingsaid characteristic information, and of selecting and recommending onlythe contents not recommended to said user having said user ID or onlythe contents recommended to but not specified by said user from amongcontents registered in the past by said specified user, contentsrecommended to said user or contents recommended to and specified bysaid user,

wherein said recommended contents are output by output means, and

said characteristic information is obtained by calculation for each itemand stored by characteristic information calculation means on the basisof contents registered in the past by each user attempting to receiverecommendation, contents recommended to said user or contentsrecommended to and specified by said user.

A twenty-fourth exemplary embodiment of the present invention relates toan information recommendation system comprising:

a content database for storing contents formed of plural pieces of datahaving plural items and attribute values corresponding thereto;

content registration means of registering said contents in said contentdatabase;

condition input means of inputting conditions represented bypredetermined items and attribute values via the Internet when saidconditions are designated through the terminal of a user;

recommendation means of selecting and recommending contents coincidentwith or similar to said input conditions from said content database; and

output means of outputting said recommended contents to said terminalvia said Internet.

A twenty-fifth exemplary embodiment of the present invention relates toan information recommendation system comprising:

a content database for storing contents formed of plural pieces of datahaving plural items and attribute values corresponding thereto;

content registration means of registering said contents in said contentdatabase;

condition input means of inputting conditions represented bypredetermined items and attribute values;

recommendation means of selecting and recommending contents coincidentwith or similar to said input conditions from said content database; and

output means of outputting said selected contents, wherein

said items are subjective items resulting from human decisions andimpressions, and/or objective items free from human decisions andimpressions.

A twenty-sixth exemplary embodiment of the present invention relates toan information recommendation system comprising:

a content database for storing contents formed of plural pieces of datahaving plural items and attribute values corresponding thereto;

content registration means of registering said contents in said contentdatabase;

condition input means of inputting conditions represented bypredetermined items and attribute values;

recommendation means of selecting and recommending contents coincidentwith or similar to said input conditions from said content database; and

output means of outputting said recommended contents, wherein

a scoring method for scoring points to each of said items depending onthe degree of similarity is predetermined in order to judge thesimilarity between said conditions to be input and each of saidcontents.

A twenty-seventh exemplary embodiment of the present invention relatesto an information recommendation system comprising:

a content database for storing contents formed of plural pieces of datahaving plural items and attribute values corresponding thereto;

content registration means of registering said contents in said contentdatabase;

condition input means of inputting conditions represented bypredetermined items and attribute values;

recommendation means of selecting and recommending contents coincidentwith or similar to said input conditions from said content database; and

output means of outputting said recommended contents, wherein

a thesaurus is used to judge the similarity between said conditions tobe input and each of said contents in the case when said attributevalues are represented by words.

A twenty-eight exemplary embodiment of the present invention relates toan information recommendation system comprising:

a content database for storing contents formed of plural pieces of datahaving plural items and attribute values corresponding thereto;

content registration means of registering said contents in said contentdatabase;

condition input means of inputting conditions represented bypredetermined items and attribute values;

recommendation means of selecting and recommending contents coincidentwith or similar to said input conditions from said content database; and

output means of outputting said recommended contents, wherein

the number of times a user attempting to receive recommendation receivesrecommendation or the content of the recommendation is determineddepending on the number of times said user carried out registration byusing said content registration means.

A twenty-ninth exemplary embodiment of the present invention relates toan information recommendation system comprising:

a content database for storing contents formed of plural pieces of datahaving plural items and attribute values corresponding thereto;

content registration means of registering said contents in said contentdatabase;

condition input means of inputting conditions represented bypredetermined items and attribute values;

recommendation means of selecting and recommending contents coincidentwith or similar to said input conditions from said content database;

output means of outputting said recommended contents, and

condition extraction means of automatically extracting said conditions,wherein

said conditions input to said condition input means are conditionsextracted by condition extraction means.

A thirtieth exemplary embodiment of the present invention relates to aninformation recommendation system comprising:

a content database for storing contents formed of plural pieces of datahaving plural items and attribute values corresponding thereto;

content registration means of registering said contents in said contentdatabase;

condition input means of inputting conditions represented bypredetermined items and attribute values;

recommendation means of selecting and recommending contents coincidentwith or similar to said input conditions from said content database;

output means of outputting said recommended contents; and

characteristic information calculation means of obtaining characteristicinformation by calculation for each of said items and storing saidcharacteristic information on the basis of contents registered in thepast by a user attempting to receive recommendation, contentsrecommended to said user or contents recommended to and specified bysaid user, wherein,

in the case of recommendation to a specific user, said recommendationmeans specifies other users whose characteristic information iscoincident or similar to said characteristic information of saidspecific user by using said stored characteristic information, andselects and recommends contents registered in the past by said otherusers, contents recommended to said other users or contents recommendedto and specified by said other users.

A thirty-first exemplary embodiment of the present invention relates toan information recommendation system comprising:

a content database for storing recipes formed of plural pieces of datahaving plural items and attribute values corresponding thereto;

content registration means of registering said recipes in said contentdatabase;

condition input means of inputting conditions represented bypredetermined items and attribute values;

recommendation means of selecting and recommending recipes coincidentwith or similar to said input conditions from said content database; and

output means of outputting said recommended contents, wherein

said content database is classified into recipe groups for dishes takenfor a meal, and

when said conditions are input, said recommendation means determines arecipe coincident with or most similar to said conditions, and selectsand determines all or part of recipes other than said determined recipe.

A thirty-second exemplary embodiment of the present invention relates toan information recommendation system comprising:

a content database for storing contents formed of plural pieces of datahaving plural items and attribute values corresponding thereto;

content registration means of registering said contents in said contentdatabase;

condition input means of inputting conditions represented bypredetermined item and attribute values;

recommendation means of selecting and recommending contents coincidentwith or similar to said input conditions from said content database;

output means of outputting said recommended contents;

characteristic information calculation means of obtaining characteristicinformation by calculation for each of said items and storing saidcharacteristic information on the basis of contents registered in thepast by a user attempting to receive recommendation, contentsrecommended to said user or contents recommended to and specified bysaid user;

type information calculation means of using said characteristicinformation obtained by calculation for each characteristic as typeinformation, while various characteristics regarding said user have beeninput; and

type judgment means of judging the type of said user attempting toreceive recommendation by comparing said characteristic information ofsaid user with said type information.

A thirty-third exemplary embodiment of the present invention relates toan information recommendation system comprising:

a content database for storing contents formed of plural pieces of datahaving plural items and attribute values corresponding thereto;

content registration means of registering said contents in said contentdata base;

condition input means of inputting conditions represented bypredetermined items and attribute values;

recommendation means of selecting and recommending contents coincidentwith or similar to said input conditions from said content database;

output means of outputting said recommended contents;

advertisement database for storing plural advertisements having relatedinformation having the same configuration as those of said items andsaid attribute values in said contents; and

advertisement specifying means of specifying advertisements coincidentwith or similar to said input conditions among advertisements stored insaid advertisement database.

A thirty-fourth exemplary embodiment of the present invention relates toan information recommendation system comprising:

a content database for storing contents formed of plural pieces of datahaving plural items and attribute values corresponding thereto;

content registration means of registering said contents in said contentdatabase;

condition input means of inputting conditions represented bypredetermined items and attribute values;

recommendation means of selecting and recommending contents coincidentwith or similar to said input conditions from said content database;

output means of outputting said recommended contents;

advertisement database for storing plural advertisements having relatedinformation having the same configuration as those of said items andsaid attribute values in said contents; and

advertisement specifying means of specifying an advertisement, which issimilar to the characteristic information of each item for a user andobtained by calculation and stored on the basis of contents registeredin the past by said user attempting to receive recommendation, contentsrecommended to said user or contents recommended to and specified bysaid user, among said advertisements stored in said advertisementdatabase. A thirty-fifth exemplary embodiment of the present inventionrelates to an information recommendation system comprising:

a content database for storing contents formed of plural pieces of datahaving plural items and attribute values corresponding thereto;

content registration means of registering said contents in said contentdatabase;

characteristic information calculation means of obtaining characteristicinformation by calculation for each of said items and storing saidcharacteristic information on the basis of contents registered in thepast by a user attempting to receive recommendation, contentsrecommended to said user or contents recommended to and specified bysaid user;

recommendation means, in the case when recommendation is performed tosaid user having an input user ID, of specifying other users whosecharacteristic information is coincident with or similar to saidcharacteristic information to said user having said user ID by usingstored characteristic information, and of selecting and recommendingonly the contents not recommended to said user having said user ID orthe contents recommended to but not specified by said user from amongcontents registered in the past by said specified user, contentsrecommended to said user or contents recommended to and specified bysaid user, in said content database; and

output means of outputting said recommended contents.

A thirty-sixth exemplary embodiment of the present invention relates toa program, in an information recommendation apparatus according to thefirst exemplary embodiment, for making a computer function as all orpart of recommendation means of selecting and recommending contentscoincident with or similar to conditions input by condition input meansof inputting said conditions represented by predetermined items andattribute values corresponding thereto designated through the terminalof a user via the Internet, from among contents formed of plural piecesof data having plural items and attribute values corresponding theretoand stored in a content database in which said contents are registeredby registration means.

A thirty-seventh exemplary embodiment of the present invention relatesto a program, in an information recommendation apparatus according toone of second to fourteenth and sixteenth to eighteenth exemplaryembodiments, for making a computer function as all or part ofrecommendation means of selecting and recommending contents coincidentwith or similar to conditions input by condition input means ofinputting said conditions represented by predetermined items andattribute values corresponding thereto, from among contents formed ofplural pieces of data having plural items and attribute valuescorresponding thereto and stored in a content database in which saidcontents are registered by registration means.

A thirty-eighth exemplary embodiment of the present invention relates toa program, in an information recommendation apparatus according to thefifteenth exemplary embodiment, for making a computer function as all orpart of recommendation means of selecting and recommending recipescoincident with or similar to conditions input by condition input meansof inputting said conditions represented by predetermined items andattribute values corresponding thereto, from among said recipes formedof plural pieces of data having plural items and attribute valuescorresponding thereto and stored in a content database in which saidrecipes are registered by registration means.

The thirty-ninth exemplary embodiment of the present invention relatesto a program, in an information recommendation apparatus according tothe twenty-third exemplary embodiment, for making a computer function asall or part of recommendation means, in the case when recommendation isperformed to said user having an input user ID, of specifying otherusers whose characteristic information coincident with or similar tosaid characteristic information to said user having said user ID byusing characteristic information, and of selecting and recommending onlythe contents not recommended to said user having said user ID or thecontents recommended to but not specified by said user from amongcontents registered in the past by said specified user, contentsrecommended to said user or contents recommended to and specified bysaid user, from among contents formed of plural pieces of data havingplural items and attribute values corresponding thereto and stored in acontent database in which said contents are registered by registrationmeans.

In the twenty-eighth exemplary embodiment of the present invention, thenumber of registration times of the user may be determined (a) bychecking the access history of the user with respect to registration or(b) by assigning the user ID of the registrant to the content and byusing the user ID.

In the twenty-ninth exemplary embodiment of the present invention, theconditions to be input to the condition input means may be thoseextracted on the basis of contents registered in the past by a user whowill receive recommendation.

In the twenty-ninth exemplary embodiment of the present invention, thesystem may be provided with a characteristic calculation means ofobtaining the characteristic of each item of the user by calculationeach time the user registers the data.

In the twenty-ninth exemplary embodiment of the present invention, theconditions to be input to the condition input means may be thoseextracted on the basis of contents recommended in the past to a user whois attempting to receive recommendation or on the basis of contentsrecommended to the user and specified by the user.

Furthermore, in the above descriptions, the system may be provided withcharacteristic calculation means of obtaining the characteristic of eachitem of the user by calculation each time the user receivesrecommendation or each time the user receives recommendation andspecifies the recommendation.

Furthermore, in the above descriptions, when the conditions areextracted from the contents, the conditions having tendencies oppositeto those of the contents may be extracted.

In the twenty-ninth exemplary embodiment of the present invention, thecondition input means may input the externally input conditions and theautomatically extracted conditions, and

the recommendation means may select contents coincident with or similarto the automatically extracted conditions from only the contentsconforming to the externally input conditions and may recommend theselected contents.

In the thirty-third and thirty-fourth exemplary embodiments of thepresent invention, the system may be provided with an advertisementcounter for counting the number of times the advertisement is specified,wherein

an advertisement rate may be charged depending on the count value of theadvertisement counter.

In the twenty-fourth to thirty-fourth exemplary embodiments of thepresent invention, the condition input means may input the conditions byusing a one-dimensional or two-dimensional interface.

In the twenty-fourth to thirty-fourth exemplary embodiments of thepresent invention, the contents recommended to the user in apredetermined period in the past may not be recommended again to theuser by the recommendation means.

In the twenty-fourth, twenty-fifth, twenty-eighth and thirty-fourthexemplary embodiments of the present invention, contents including acharacter string coincident with the character string included in theinput conditions may be selected from the content database andrecommended.

As an embodiment, the present invention comprises network interfacemeans connected to a terminal via the Internet to perform datacommunication, a content database for storing information to berecommended, recommendation condition input means of accepting theconditions of information desired to be recommended, contentrecommendation means of selecting contents conforming to recommendationconditions input from the recommendation condition input means, andcontent output means of outputting contents selected by the contentrecommendation means, wherein groups of the titles of contents and theirobjective and subjective characteristic amounts are registered ascontent data, and appropriate contents are recommended depending on theobjective or subjective recommendation conditions from a user.

Furthermore, as an embodiment, the present invention comprises networkinterface means connected to a terminal via the Internet to perform datacommunication, a content database for storing information to berecommended, user identification means of identifying a user who madeaccess at the time of access from the terminal, access history controlmeans of controlling the access history of the user, contentregistration means of accepting registration of new contents from theterminal, recommendation condition input means of accepting theconditions of information desired to be recommended, contentrecommendation means of selecting contents conforming to recommendationconditions input from the recommendation condition input means, andcontent output means of outputting contents selected by the contentrecommendation means, wherein information is recommended depending onthe registration results of content data of the user, thereby to urgethe user to register content data.

Furthermore, as an embodiment, the present invention has a configurationwherein, as content data, items regarding the cause-effect relationsbetween the state before the content data is selected and the stateafter the content data is selected are registered in the contentdatabase, and the state before the content data is selected or a statedesired to be obtained after the content data is selected is input,thereby to recommend content data depending on the state before thecontent data is selected or the state desired to be obtained after thecontent data is selected.

Furthermore, as an embodiment, the present invention has a configurationwherein, as content data, items in view of a person who generated thecontent data and items in view of a person who selects the content dataare registered in the content database, and the viewpoint of the personwho generates the content data or the viewpoint of the person whoselects the content data is input, thereby to recommend content datadepending on the viewpoint of the person who generates the content dataand the viewpoint of the person who selects the content data.

Furthermore, as an embodiment, the present invention has a configurationwherein items capable of being represented quantitatively are used asthe conditions of information desired to be recommended, and therecommendation conditions are input by using a pointer or a slidercapable of being moved up-and-down or right-and-left, wherein thequantitative values of the recommendation conditions can be inputvisually.

Furthermore, as an embodiment, the present invention has a configurationwherein two kinds of items capable of being represented quantitativelyare used as the conditions of information desired to be recommended, andthe quantitative values of the two kinds of recommendation conditionscan be input at a time by using a pointer capable of being movableup-and-down and right-and-left on a two-dimensional plane having theabscissa representing a first condition and the ordinate representing asecond condition, whereby the two quantitative values in therecommendation conditions can be input visually at a time.

Furthermore, as an embodiment, the present invention comprises networkinterface means connected to a terminal via the Internet to perform datacommunication, a content database for storing information to berecommended, user identification means of identifying a user who madeaccess at the time of access from the terminal, access history controlmeans of controlling the access history of the user, contentregistration means of accepting registration of new contents from theterminal, recommendation condition extraction means of extractingrecommendation conditions from the characteristic amounts of contentsregistered previously by the user, recommendation condition input meansof accepting recommendation conditions extracted by the recommendationcondition extraction means, content recommendation means of selectingcontents conforming to recommendation conditions input from therecommendation condition input means, and content output means ofoutputting contents selected by the content recommendation means,wherein recommendation conditions are extracted automatically from thedata registered by the user, whereby the recommendation conditions arenot required to be input at the time of information recommendation.

Furthermore, as an embodiment, the present invention comprises networkinterface means connected to a terminal via the Internet to perform datacommunication, a content database for storing information to berecommended, recommendation condition input means of accepting theconditions of information desired to be recommended, contentrecommendation means of selecting contents conforming to recommendationconditions input from the recommendation condition input means, contentoutput means of outputting contents selected by the contentrecommendation means, and an advertisement database for providingadvertisement data, wherein, when recommended information is given to auser, an advertisement related thereto is displayed simultaneously, andthe number of times the advertisement is displayed is counted, wherebyan advertisement rate can be charged to the advertiser of theadvertisement depending on the number of times the advertisement isdisplayed.

Furthermore, as an embodiment, the present invention has a configurationwherein information is transmitted and received by using a WWW browseror electronic mail to exchange data between an informationrecommendation apparatus and the terminals of users via the Internetused as a medium. Hence, the recommendation of information can bereceived by terminals, such as personal computers, portable informationterminals and portable telephones.

Furthermore, as an embodiment, the present invention comprises networkinterface means connected to a terminal via the Internet to perform datacommunication, recommendation condition input means of accepting theconditions of information desired to be recommended, a content databasefor storing information to be recommended, user identification means ofidentifying a user who made access at the time of access from theterminal, access history control means of controlling the access historyof the user, content registration means of accepting registration of newcontents from the terminal, user characteristic information calculationmeans of extracting the tendencies of favorite contents of the user fromthe characteristic amounts of the contents registered previously by theuser or from the characteristic amounts of the contents selectedpreviously by the user, user characteristic information database inwhich user characteristic information extracted by the usercharacteristic information calculation means is registered, similar userselection means of selecting users having similar tendencies of favoritecontents in comparison with user characteristic information, contentrecommendation means of selecting contents conforming to recommendationconditions input from the recommendation condition input means, andcontent output means of outputting contents selected by the contentrecommendation means, wherein, when the recommendation of content datais requested, the tendencies of favorite contents of the user areextracted from the characteristic amounts of content data registered orselected previously by the user, the tendencies are compared betweenusers, users similar to the user are selected, and content data isrecommended depending on recommendation conditions designated by theuser from among content data registered previously by the selectedsimilar users, whereby the favorite contents of users having similarpreferences are recommended.

Furthermore, as an embodiment, the present invention has a configurationwherein, by inputting the title of content data registered in thecontent database or by inputting a part of a character stringconstituting a characteristic amount for characterizing the content dataas a recommendation condition, content data, the title of which or apart of a character string constituting a characteristic amount of whichis partially coincident with the recommendation condition, isrecommended as content data to be recommended, whereby various contentsrelated to or derived from a certain content are recommended.

Furthermore, as an embodiment, the present invention has a configurationwherein one set of content records is formed of at least two or morecontents, many content records formed as described above are registeredin a content database, a title is input as a recommendation condition,sets of content records including the content input as therecommendation condition are first selected as content data to berecommended, contents not conforming to the recommendation condition areoutput as contents to be recommended from among two or more contentsconstituting each of content records, whereby contents suited to be usedas a set when combined with a certain content are recommended.

Furthermore, as an embodiment, the present invention comprises networkinterface means connected to a terminal via the Internet to perform datacommunication, recommendation condition input means of accepting theconditions of information desired to be recommended, a content databasefor storing information to be recommended, user identification means ofidentifying a user who made access at the time of access from theterminal, access history control means of controlling the access historyof the user, content registration means of accepting registration of newcontents from the terminal, type information characteristic informationcalculation means of extracting the tendencies of favorite contents ofthe user conforming to a certain condition from the characteristicamounts of the contents registered previously by the user or from thecharacteristic amounts of the contents selected previously by the user,type information database in which type information obtained bycalculation by the type information calculation means is registered,type information selection means of selecting a type having similartendencies of favorite contents of the user in comparison with typeinformation, content recommendation means of selecting contentsconforming to recommendation conditions input from the recommendationcondition input means, and content output means of outputting contentsand type information selected by the content recommendation means,wherein, when the recommendation of content data is requested, thetendencies of favorite contents of the user are extracted from thecharacteristic amounts of content data registered or selected previouslyby the user, the tendencies of favorite contents of the user conformingto a certain condition are obtained by calculation at the same time, thetendencies of contents of the user are compared with type information,type information similar to that of the user is selected, and contentdata depending on recommendation conditions designated by the user isrecommended, and type information is displayed, whereby typeinformation, which corresponds to the user and on which informationrecommendation is based, is displayed at the time of informationrecommendation.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a view showing a system configuration in accordance withEmbodiment 1 of the present invention;

FIG. 2 is a flowchart showing information recommendation operation inaccordance with Embodiment 1 of the present invention;

FIG. 3 is a view showing an example of content data in accordance withEmbodiment 1 of the present invention;

FIG. 4 is a flowchart showing specific information recommendationoperation in accordance with Embodiment 1 of the present invention;

FIG. 5 is a view showing a system configuration in accordance withEmbodiment 2 of the present invention;

FIG. 6 is a flowchart showing registration operation in accordance withEmbodiment 2 of the present invention;

FIG. 7 is a flowchart showing information recommendation operation inaccordance with Embodiment 2 of the present invention;

FIG. 8 is a view showing a system configuration in accordance withEmbodiment 3 of the present invention;

FIG. 9 is a flowchart showing operation in accordance with Embodiment 3of the present invention;

FIG. 10 is a list indicating the tendencies of user's favoriteingredients in accordance with Embodiment 3 of the present invention;

FIG. 11 is a view showing an example of content data in accordance withEmbodiment 3 of the present invention;

FIG. 12 is a view showing a system configuration in accordance withEmbodiment 4 of the present invention;

FIG. 13 is a flowchart showing operation in accordance with Embodiment 4of the present invention;

FIG. 14 is a view showing an example of advertisement data in accordancewith Embodiment 4 of the present invention;

FIG. 15 is a view showing an example of a display in accordance withEmbodiment 1 of the present invention;

FIG. 16 is a view showing an example of a display in accordance withEmbodiment 4 of the present invention;

FIG. 17 is a view showing a recommendation condition input interface inaccordance with Embodiment 2 of the present invention;

FIG. 18 is a view showing another recommendation condition inputinterface in accordance with Embodiment 2 of the present invention;

FIG. 19 is a view showing a system configuration in accordance withEmbodiment 5 of the present invention;

FIG. 20 is a flowchart showing registration operation in accordance withEmbodiment 5 of the present invention;

FIG. 21 is a flowchart showing information recommendation operation inaccordance with Embodiment 5 of the present invention;

FIG. 22 is a view showing a system configuration in accordance withEmbodiment 6 of the present invention;

FIG. 23 is a flowchart showing information recommendation operation inaccordance with Embodiment 6 of the present invention;

FIG. 24 is a view showing an example of content data in accordance withEmbodiment 7 of the present invention;

FIG. 25 is a flowchart showing information recommendation operation inaccordance with Embodiment 7 of the present invention;

FIG. 26 is a view showing a system configuration in accordance withEmbodiment 8 of the present invention;

FIG. 27 is a flowchart showing registration operation in accordance withEmbodiment 8 of the present invention;

FIG. 28 is a flowchart showing information recommendation operation inaccordance with Embodiment 8 of the present invention;

FIG. 29 is a view showing an example of a display in accordance withEmbodiment 8 of the present invention;

REFERENCE NUMERALS

-   1 network interface means-   2 content database-   3 recommendation condition input means-   4 content recommendation means-   5 content output means-   6 user identification means-   7 access history control means-   8 content registration means-   9 recommendation condition extraction means-   10 advertisement database-   11 similar user selection means-   12 user characteristic information database-   13 user characteristic information calculation means-   14 type information selection means-   15 type information calculation means-   16 type information database-   100 terminal-   200 advertisement-   210 slider-   220 pointer-   500 Internet-   1000 information recommendation apparatus

DETAILED DESCRIPTION OF THE INVENTION

Embodiments in accordance with the present invention will be describedbelow referring to the accompanying drawings.

Embodiment 1

First, Embodiment 1 in accordance with the present invention will bedescribed below.

FIG. 1 is a view showing the system configuration of an informationrecommendation apparatus 1000 in accordance with Embodiment 1 of thepresent invention. In FIG. 1, numeral 1 designates network interfacemeans connected to a terminal 100 via the Internet 500 to perform datacommunication, numeral 2 designates a content database for storingrecommended information, numeral 3 designates recommendation conditioninput means of accepting the conditions of information desired to berecommended, numeral 4 designates content recommendation means ofselecting contents conforming to the recommendation conditions inputfrom the recommendation condition input means 3, and numeral 5designates content output means of outputting contents selected by thecontent recommendation means 4.

Furthermore, since a hardware configuration by which the systemconfigured as described above is operated is basically identical to thatof a general-purpose computer system, the explanation of theconfiguration is omitted.

The operation of the information recommendation apparatus 1000 operatingby using the system configured as described above will be explainedbelow. The explanation will be given referring to a flowchart shown inFIG. 2 by using a dish recommendation system as an example.

(Step A1)

Recommendation conditions input by a user through the terminal 100 aretransmitted via the Internet 500 and received by the network interfacemeans 1 of the information recommendation apparatus 1000.

For example, when an item “ingredients” and its attribute values “beef,carrot and onion” are input as recommendation conditions by using theterminal 100, they are transmitted to the information recommendationapparatus 1000 and input to the recommendation condition input means 3.The item and the attribute values will be explained later.

(Step A2)

Contents conforming to the recommendation conditions received at step A1are selected from the content database 2.

Data of recipes for dishes has been registered as content data in thecontent database 2. A specific example of recipe data is shown in FIG.3.

In FIG. 3, each of the contents comprises plural pieces of data having aplurality of items and attribute values corresponding to the items. Inother words, items, such as “data ID,” “recipe name,” “cooking method,”“ingredients,” “cooking time,” “calories” and “impression,” areavailable as items designating the attributes of the data. Hamburger iswritten as an attribute value corresponding to the item “recipe name,”and a hamburger cooking method is written as an attribute valuecorresponding to the item “cooking method.” Furthermore, the attributevalues corresponding to the items “ingredients,” “cooking time” and“calories” are also written specifically. These items “ingredients,”“cooking time” and “calories” are used to characterize the recipe forthe hamburger and are designated by the names of specific ingredientsand physical amounts. These are referred to as objective characteristicamounts.

In addition, the attribute values corresponding to the item “impression”are characteristic amounts from subjective viewpoints of the author orthe registrant of the content of this recipe. As examples of suchcharacteristic amounts, “the grade of easiness” is 4 and “the grade oflightness” is 2 in accordance with a five-grade evaluation method. Theseare referred to as subjective characteristic amounts.

Furthermore, the items and attribute values input as recommendationconditions at step A1 are configured so as to be similar to the itemsand attribute values of the content shown in FIG. 3.

These subjective characteristic amounts may be described quantitatively,such as an attribute value “the grade of easiness=4.” Alternatively, thesubjective characteristics may be simply described qualitatively, suchas attribute values “easy” and “light.”

In this Embodiment, “impression” is provided as an item representing asubjective characteristic, and the attribute values corresponding tothis item are given by “the grade of easiness=4” and “the grade oflightness=2” in the above-mentioned descriptions. However, instead ofthis, “the grade of easiness” may be provided as an item, and theattribute value corresponding thereto may be represented by 4. Inaddition, “the grade of lightness” may be provided as an item, and theattribute value corresponding thereto may be represented by 2. In otherwords, a plurality of items representing subjective characteristics maybe provided, and the attribute values corresponding to the itemsrespectively may be represented by numeral values.

Abundant pieces of recipe data are registered in the content database 2in the above-mentioned format. The content recommendation means 4compares the recommendation conditions input at step A1 with theattribute values of the recipe data, and selects content data havinghigh degrees of coincidence.

(Step A3)

The contents (recipe data) selected at step A2 are transmitted from thecontent output means 5 to the terminal 100 via the network interfacemeans 1. The contents and lists are displayed on the display screen ofthe terminal 100.

The displaying method of the terminal 100 is not required to haveparticular characteristics. However, for example, recipes are displayedon a WWW browser as shown in FIG. 15. When all pieces of recommendedcontent data cannot be displayed once, they can be seen by scrolling thedisplay of the WWW browser or by turning its pages.

The contents are recommended by the above-mentioned procedure. As aspecific method of selecting the recommended contents at step A2, themethod shown in FIG. 4 is used.

(Step A21)

From the content database 2, one of the contents is selected.

(Step A22)

The selected content data has been registered in a format indicated inFIG. 3.

The attribute values “beef, carrot and onion” corresponding to the item“ingredients” used as recommendation conditions are compared with theattribute values corresponding to the item “ingredients” of the contentdata. The number of coincident attribute values is then counted. Forexample, when the currently selected content is the hamburger shown inFIG. 3, “onion” in the attribute values of the item “ingredients”coincides with the attribute value corresponding to the item“ingredients” of the recommendation conditions. Hence, the number of thecoincident attribute values is one. This count value multiplied by threeis set as an evaluation value for the item “ingredients” of thehamburger.

In other words, in order to judge the similarity between inputrecommendation conditions and the contents, a scoring method by whicheach item is scored depending on the degree of similarity has beendetermined beforehand. The evaluation value for the item “ingredients”of the above-mentioned hamburger is calculated by this scoring method.That is to say, in the above-mentioned example, by the scoring methodfor the item “ingredients”, the number of the coincident attributevalues corresponding to the item “ingredients” is multiplied by three,and the result is used as the score of the item “ingredients”. Bycarrying out scoring by using this scoring method, the evaluation valueof the item “ingredients” for the hamburger becomes three as describedabove.

(Step A23)

Next, a confirmation is made as to whether contents not yet providedwith evaluation values for the recommendation conditions are present inthe content database 2 or not. If such data is present, the processingsequence returns to step A21, and the processing for the next contentdata continues.

If the processing is completed concerning all of content data, theprocessing sequence advances to step A24.

(Step A24)

In the end, a fixed number of contents in decreasing order of theevaluation values assigned at step A22 are selected. This fixed numbermay have been determined beforehand by the system. The fixed number maybe input as the maximum recommendation number by the user at the timewhen the recommendation conditions are input.

By taking the above-mentioned procedure, it is possible to select dataconforming to the recommendation conditions from among all of contentdata in the content database 2 and to display the data.

A scoring method has also been predetermined for each of other items. Itis possible to calculate the evaluation value of the item depending on ascore. Since the evaluation value of each item is given by a score, evenif a recommendation condition includes a plural kinds of items andattribute values corresponding to the items, contents comprehensivelyconforming to the recommendation condition can be recommended bycompiling the evaluation values of the items and by selecting the fixednumber of contents in decreasing order of the evaluation values.

In this embodiment, the objective and qualitative condition meaning thatthe attribute values of the item “ingredients” are “beef, carrot andonion” is used as a recommendation condition as described above.However, the present invention is not limited to this embodiment. Forexample, objective and quantitative conditions may also be used.

More specifically, a condition “cooking time 30 minutes” may be used asa recommendation condition. The condition “cooking time 30 minutes” isassumed to mean that the item of the recommendation condition is“cooking time,” and that the attribute corresponding to the item is 30minutes.

In this case, as a recommended content selection method, withoutcounting the number of coincident attribute values in the content dataat step A22 of FIG. 4, it may be possible to use a method of evaluatingthe absolute value of the difference between the attribute value of therecommendation condition and the attribute value of each of contents.For example, when the absolute value of the difference between theattribute values is 5 or less, a score of 2 is given, when the absolutevalue is 6 or more and 10 or less, a score of 1 is given, and when theabsolute value is 11 or more, a score of 0 is given. In this case, ifthe attribute value of the recommendation condition is 30 minutes and ifthe cooking time for a recipe is 20 minutes, the absolute value of thedifference between the attribute values is 10. Hence, a score of 1 isgiven as the evaluation value. As described above, the scoring methodfor the item “cooking time” is different from the scoring method for theabove-mentioned item “ingredients.”

Furthermore, in the above example, information recommendation isperformed on the basis of objective characteristic amounts. However,information recommendation may be performed on the basis of subjectivecharacteristic amounts. For example, “the grade of easiness 3, the gradeof lightness 4” may be input as a subjective and quantitative conditionused as a recommendation condition.

The expression “the grade of easiness 3, the grade of lightness 4” isassumed to mean that the item of the recommendation condition is“impression” and that the attribute values corresponding to the item arethe grade of easiness 3 and the grade of lightness 4.

In the case when a plurality of items representing subjectivecharacteristics, such as “the grade of easiness” and “the grade oflightness,” are provided, and when an attribute value corresponding toeach item is represented by a numeral value, “the grade of easiness 3,the grade of lightness 4” means that the recommendation condition hastwo items “the grade of easiness” and “the grade of lightness,” and thatthe attribute value corresponding to “the grade of easiness” is 3 andthe attribute value corresponding to “the grade of lightness” is 4.

In addition, recommendation conditions can be represented by either ofthese methods. However, the recommendation conditions are assumed torepresent subjective characteristics in accordance with the same methodwherein contents represent subjective characteristics.

In this case, as a recommended content selection method, withoutcounting the number of coincident attribute values at step A22 of FIG.4, it may be possible to use a method of evaluating the absolute valueof the difference between the attribute value corresponding to the itemrepresenting the subjective item of the recommendation condition and theattribute value corresponding to the item identical to the item in eachof contents represented in the recommendation condition. For example,when the absolute value of the difference between the evaluation valuesis 0 or less, a score of 2 is given, when the absolute value is 1, ascore of 1 is given, and when the absolute value is 2 or more, a scoreof 0 is given.

In the case of the hamburger shown in FIG. 3, in the recommendationcondition “the grade of easiness 3, the grade of lightness 4,” that is,in the recommendation condition wherein the attribute value of the item“the grade of easiness” is 3 and the attribute value of the item “thegrade of lightness” is 4, the absolute value of the difference betweenthe evaluation values is 1 for the grade of easiness. Hence, a score of1 is given. Furthermore, since the absolute value of the difference is 2for the grade of lightness, a score of 0 is given. As a result, thetotal value 1 is given as the evaluation value.

Furthermore, even in a case based on a subjective characteristic amount,when just a qualitative attribute value such as “easiness” is providedas content data, instead of the quantitative amount “the grade oflightness 4” in FIG. 3, it is needless to say that the method based onthe coincidence of items can be used, just as in the embodiment of thepresent invention.

In the case when an attribute value is a qualitative value, instead ofusing a method wherein evaluation values are added depending on thecomplete coincidence of attribute values, it may be possible to use amethod wherein a thesaurus in which similarity between attribute valueshas been defined beforehand so that an evaluation value can be providedin accordance with the similarity, even when the attribute value of eachof content data does not completely coincide with the attribute value ofa recommendation condition.

In other words, the thesaurus is a list of words classifiedsystematically in tree structures. It is thus possible to understandthat words belonging to the same category are more similar to oneanother than those not belonging to the same category with respect toexamples and meanings. The categories are divided from a shallow levelto a deep level, step by step. For this reason, it is possible to saythat the similarity in the case when two different words belong to thesame category up to a deep level is higher than the similarity in thecase when two different words belong to the same category only up to ashallow level.

Accordingly, for example, when two words used as attribute values belongto the same category up to the first level of the thesaurus, thesimilarity is set at 0%. When two words belong to the same category upto the second level of the thesaurus, the similarity is set at 20%. Whentwo words belong to the same category up to the third level of thethesaurus, the similarity is set at 40%. When two words belong to thesame category up to the fourth level of the thesaurus, the similarity isset at 60%. When two words belong to the same category up to the fifthlevel or more of the thesaurus, the similarity is set at 80%. When twowords coincide completely with each other, the similarity is set at100%.

When the similarity between attribute values is defined as describedabove, it is assumed that “exciting” is included in the attribute valuecorresponding to the item “impression” of the recommendation condition,for example. On the other hand, it is also assumed that “enjoyable” isincluded as the attribute value corresponding to the item “impression”of content data, but “exciting” is not included. In this case, it isassumed that the similarity between “exciting” and “enjoyable” isobtained by using the thesaurus. In addition, it is also assumed thatthis similarity is 80%. The obtained similarity is converted into ascore by using a rule predetermined to convert the similarity into ascore. When it is assumed that the similarity 80% is converted into ascore of 5, the evaluation value becomes a score of 5. Furthermore, itis assumed that “delicious” is included in the item “impression” of thecontent, instead of the attribute value “enjoyable” in theabove-mentioned example. In this case, the similarity between “exciting”and “delicious” is obtained by using the thesaurus. It is assumed thatthis similarity is 60%. The obtained similarity is converted into ascore by using the above-mentioned rule for converting the similarityinto a score. When it is assumed that the similarity 60% is convertedinto a score of 3, the evaluation value becomes a score of 3. Hence, theevaluation value of the item “impression” having the attribute value“delicious” becomes lower than the evaluation value of the item“impression” having the attribute value “enjoyable”.

It is needless to say that the above-mentioned various methods can becombined as a matter of course. For example, recommendation conditionsincluding a plurality of recommendation conditions, such as “beef,carrot and onion,” “cooking time 30 minutes,” “the grade of easiness 3,the grade of lightness 4” and “exiting,” can be input as recommendationconditions. Evaluation values corresponding to these recommendationconditions respectively can be determined by the above-mentioned variousmethods. A fixed number of contents should only be selected from thecontents having high total scores of the respective evaluation values indecreasing order.

With the above-mentioned system configuration and operation, it ispossible to accomplish an information recommendation apparatus capableof recommending data conforming to the request of the user from amongcontent data stored in the content database 2. By providing content datahaving subjective characteristic amounts as attributes in particular,information recommendation can be carried out according to subjective orsensitive recommendation conditions, such as “easy-cooking dish,” “lightdish” and “enjoyable dish.”

In this embodiment, it is explained that a recommendation condition hasan item and an attribute value corresponding thereto. However, this doesnot mean that it is necessary to input the item and the attribute valuecorresponding thereto without fail when the user inputs therecommendation condition. In other words, when the user inputs arecommendation condition through the terminal, the user occasionallyinputs only the attribute values of the item “ingredients,” such as“beef, carrot and onion.” In this case, GUI displayed on the terminal ofthe user specifies the item to which the attribute values of therecommendation condition input by the user corresponds. Morespecifically, the GUI displays a message “Input ingredients you desire.”When the user inputs “beef, carrot and onion” in response to themessage, the GUI can assume that “beef, carrot and onion” are attributevalues corresponding to the item “ingredients.” As described above, itis not limited that the user must input an item and an attribute valuecorresponding thereto as one set. However, when the informationrecommendation apparatus of this embodiment performs processinginternally, an item and an attribute value corresponding thereto arerepresented as one set at all times, since the item corresponding to theattribute value is specified by the above-mentioned GUI and the like.

The recommendation condition input means 3 of this embodiment is anexample of condition input means of the present invention. The contentrecommendation means 4 of this embodiment is an example ofrecommendation means of the present invention. The content data of thisembodiment is an example of contents of the present invention.

Embodiment 2

Next, Embodiment 2 in accordance with the present invention will bedescribed.

FIG. 5 is a view showing the system configuration of an informationrecommendation apparatus 1000 in accordance with Embodiment 2 of thepresent invention.

The configuration shown in FIG. 5 is obtained by adding useridentification means 6, access history control means 7 and contentregistration means 8 to the system configuration of Embodiment 1 shownin FIG. 1.

In FIG. 5, numeral 1 designates network interface means, numeral 2designates a content database, numeral 3 designates recommendationcondition input means, numeral 4 designates content recommendationmeans, numeral 5 designates content output means, numeral 6 designatesthe user identification means of identifying a user who made access atthe time of access from a terminal, numeral 7 designates the accesshistory control means of controlling the access history of the user, andnumeral 8 designates the content registration means of acceptingregistration of new contents from the terminal.

Furthermore, since a hardware configuration by which the systemconfigured as described above is operated is basically identical to thatof a general-purpose computer system, the explanation of theconfiguration is omitted.

The operation of the information recommendation apparatus 1000 operatingby using the system configured as described above will be explainedbelow.

In this Embodiment, first, the user must register content data beforethe user receives recommendation of information.

A content data registration procedure will be described below referringto a flowchart shown in FIG. 6.

(Steps B1 and B2)

When the user gains access to the information recommendation apparatus1000 to register contents by using the terminal 100, the useridentification means 6 requests the user to input his or her user ID. Ifthe user has no user ID, it is judged that the user is using theinformation recommendation apparatus for the first time, and a user IDis issued to the user at step B2.

When the user has his or her user ID, the user inputs the user ID, andthe processing sequence advances to step B3.

When the user ID is issued at step B2, a password may also be issuedtogether with the user ID. In this case, the user must input both theuser ID and the password at step B1.

(Step B3)

The user registers content data. In the case of a recipe for a dish, theuser enters the attribute values corresponding to items, such as “recipename,” “cooking method,” “ingredients,” “cooking time” and “impression,”and registers them.

At the time of the registration, the WWW browser displayed on thedisplay screen of the terminal 100 is used. Filling spaces in apredetermined format enters content data. After the entry, the userpresses a “registration” button or the like displayed on the WWWbrowser, whereby the content registration means 8 registers the contentdata as new content data in the content database 2. At this time, thecontent data is endowed with a data ID.

(Step B4)

When the content data is registered at step B3, for the user having thecurrently registered user ID, the access history control means 7 renewsand stores information, such as the date of content data registration,the data ID of the registered data and the quantity of the contentsregistered by the user.

(Step B5)

It is confirmed as to whether the user registers additional new contentdata or not. When registration is continued, the processing sequencereturns to step B3. When registration is stopped, the sequentialregistration processing ends.

The user registers content data as described above. By registeringcontent data, the quantity of content data registered in the contentdatabase 2 increases, and the history of user registration is renewed atthe same time.

As entry items at the time when the user registers content data, variousitems other than those shown in FIG. 3 can be designated. For example,the weather on the date for taking a meal, the date for taking a meal(the day of the week, Christmas, birthday or other special days), theplace for taking a meal, the time period for taking a meal, the personwho prepared a meal, the persons who took meals together, cookingutensils used, the degree of cooking by hand, family structure,occupations, the place for buying ingredients, the budget for a meal,the time required for preparing a meal, the ordinary frequency ofcooking (how many days in a week), etc. can be designated as objectiveitems.

In addition, various items can also be designated as subjective items.For example, the comfortableness of the weather on the date for taking ameal, the feeling on the date, the physical condition on the date, thereason for selecting a dish, the situation of a meal (taking a meallonely, lively, specially or enjoyably), the degree of satisfaction at ameal, the reactions of persons having taken meals, the physicalcondition after eating, the feeling after eating, etc. can be designatedas subjective items. Furthermore, items regarding the concept of eatingin the background of the user can also be designated. For example, thefondness to eating, the fondness to cooking, important factors in meals(taste, nourishment, healthfulness, budget, easiness, etc.), etc. can bedesignated. Hence, the user may enter some or all of these items at thetime of registration.

Furthermore, as an entry method, the subjective items in particular maybe entered qualitatively by using adjectives and the like (for example,“an enjoyable delicious meal”). Alternatively, the subjective items maybe entered quantitatively by using adjectives and the gradescorresponding to the adjectives (for example, “the grade of pleasure=3,”“the grade of deliciousness=5,” etc).

Furthermore, the above-mentioned entry items can be classified in viewof cause-effect relations in meals, and the items may be entered fromthis viewpoint.

More specifically, items regarding “why did you select the dish?” or“how was your condition before eating?” are set as entry items. Forexample, the feeling on the date, the weather on the date, the reasonfor selecting the dish, the physical condition before eating, etc. canbe designated.

Furthermore, items regarding “what happened after eating” are set asentry items. For example, the situation of the meal, the degree ofsatisfaction at the meal, the reactions of persons having taken themeal, the physical condition after eating, the feeling after eating,etc. can be designated.

Furthermore, the above-mentioned entry items can be classified in viewof cause-effect relations in meals, such as from “the viewpoint of theperson who prepared the dish” or “the viewpoint of the person who tookthe dish,” and the items may be entered from the viewpoint.

For example, the cooking utensils used to prepare a dish, the place forbuying ingredients, etc. are items classified from the viewpoint of theperson who prepared the dish. The reason for selecting the dish, thedegree of satisfaction at the meal, the physical condition after eating,the feeling after eating and the like are items classified from theviewpoint of the person who took the dish.

Next, an operation by which the user receives the recommendation ofinformation will be described below referring to a flowchart shown inFIG. 7.

(Step C1)

When the user gains access to the information recommendation apparatusto receive the recommendation of information by using the terminal 100,the user identification means 6 requests the user to input his or heruser ID. If the user has no user ID, the user cannot use therecommendation of information, and the processing sequence ends. Whenthe user ID is input and it is judged to be valid, the processingsequence advances to step C2.

At this time, the user identification means 6 may request the user toinput his or her password as well as the user ID. In this case, if thepassword is valid, the processing sequence advances to step C2. If thepassword is wrong, the processing sequence ends.

(Step C2)

Next, a judgment is made as to whether the user has satisfied acondition for receiving recommended information. The judgment is made bythe access history control means 7 depending on the history as to thenumber of content data registered by the user so far and as to thenumber of times the user received the recommendation of information sofar. For example, it is assumed that a rule wherein the user can receivethe recommendation of information five times each time the userregisters a piece of content data has been determined beforehand. Inthis case, when two pieces of content data have been registeredaccording to the user's access history in the access history controlmeans 7, the user has a right to receive the recommendation ofinformation ten times. Hence, if the number of informationrecommendation times is nine or less, the user satisfies the conditionfor receiving the recommendation of information, and the processingsequence advances to step C3. If the number of informationrecommendation times is ten or more, the processing sequence ends.

(Steps C3 to C5)

When the user satisfies the condition for receiving the recommendationof information at step C2, the system accepts a recommendation conditionfrom the user, selects recommended information from the content database2, and displays the information on the terminal of the user. Theseprocedures are similar to those at steps A1 to A3 in FIG. 2.

The items for the recommendation condition are not limited to the itemsindicated in FIG. 3. All objective and subjective items registeredtogether with recipe data can be used as conditions. Hence, all theitems exemplified in the explanation of recipe registration can be usedas recommendation conditions.

Furthermore, at the time of inputting quantitative recommendationcondition items, the user can easily perform input by using an interfacehaving the shape of a slide bar shown in FIG. 17. In FIG. 17, the inputinterface is configured to have the shape of a slide bar to indicate acooking time. A portion 210 is moved so as to slide in order todesignate a cooking time. In this example, the cooking time becomesshorter as the portion 210 is moved to the left, and becomes longer asthe portion 210 is moved to the right. Specific cooking time values arealso written as guide values. In FIG. 17, the slide bar indicatesexactly 30 minutes. The indication of the guide values may be omitted.

Furthermore, in order to make two qualitative items visuallyunderstandable and to make the input of the two qualitative items easy,a two-dimensional plane having two axes for two items is formed as shownin FIG. 18, and the user is requested to designate a place in the plane.Hence, the user can perform input for two items by one operation,whereby the input becomes less burdensome for the user. In FIG. 18, twoitems, that is, cooking time and the number of dishes, are exemplified,and the abscissa represents cooking time, and the ordinate representsthe number of dishes. In this example shown in FIG. 18, unlike FIG. 17,no guide values are written on the axes. However, guide values may bewritten as a matter of course. When they are not written, the centralportion indicates an average value on each of the abscissa and theordinate. The average value is increased or decreased by moving theplace in each of the directions of the abscissa and the ordinate. InFIG. 18, numeral 220 designates a pointer for designating the two-axisvalues. In this figure, the cooking time is designated to be slightlyshorter than the average value, and the number of dishes is slightlylarger than the average value.

As these qualitative items, objective items, such as a cooking time, thenumber of dishes, calories, the cost of ingredients, family structure(the number of family members) and the temperature on the date, can bedesignated. Furthermore, if, even among subjective items, items havingregistered quantitatively in the database, such as the grade ofdeliciousness, the grade of satisfaction, the grade of pleasure, thegrade of heaviness and the grade of refreshment, are registered, forexample, as in “the grade of deliciousness=3,” the subjective items canalso be treated as quantitative items.

In addition, the two items for forming the two-dimensional plane shownin FIG. 18 can be selected arbitrarily from among the above-mentionedquantitative items. Various combinations of the items are made possible.

Even when entry items have been set in view of cause-effect relationsbefore and after eating, items capable of being representedquantitatively can be searched for in a similar way.

Furthermore, if subjective evaluation items (the grade of satisfactionof a dish and the like) have been set as content items, and when aplurality of contents conforming to a designation condition of the userare present, the relative order of the subjective evaluation itemsshould be obtained, and then contents useful (satisfactory) for the usershould be recommended as a matter of course.

(Step C6)

When information is recommended at step C5, the access history controlmeans 7 renews and stores information for the current user, such as thedate when the recommendation of information is received, the data ID ofrecommended information and the number of information recommendationtimes.

With the above-mentioned system configuration and operation, it ispossible to accomplish an information recommendation apparatus whereincontent data stored in the content database 2 can be made complete anddata conforming to the request of the user can be recommended.

In addition, the user is asked to enter objective items and subjectiveitems at the time of content registration. Hence, the user can designatethe conditions of registered contents by using the objective items andsubjective items, and can receive the recommendation of the contents.

Furthermore, the user is asked to enter subjective evaluation items atthe time of content registration. Hence, the relative merits of theregistered contents can be determined. By considering this matter at thetime of content recommendation, contents having higher merits can berecommended.

Furthermore, the user is asked to enter items regarding cause-effectrelations before and after eating at the time of content registration.Hence, the user can designate conditions in view of cause-effectrelations of a meal, such as “a dish to be taken when not feeling well”and “a dish giving pleasant feelings after eating,” and the user canreceive the recommendation of contents.

Furthermore, the user is asked to enter items regarding the person whoprepares a dish or the person who eats a dish at the time ofregistration. Hence, the user can designate conditions from thestandpoint of the person who prepares a dish or the person who eats adish, and can receive the recommendation of registered contents.

Moreover, the user can search for contents easily with an interfacecapable of making input easy by using registration items that can bemade quantitative.

In this embodiment, as a condition of receiving recommended information,information recommendation can be received five times for one piece ofcontent data. However, the present invention is not limited to this.This condition can be set as desired. For example, to attract attentionof the user, the condition may be set so that recommended informationcan be obtained for the first two times even when no content data hasbeen registered.

Furthermore, in this embodiment, a judgment is made as to whether theuser has satisfied a condition for receiving recommended information.This judgment is made by the access history control means 7 depending onthe history as to the number of content data registered by the user sofar and as to the number of times the user received the recommendationof information so far. However, the present invention is not limited tothis. Content data may be endowed with the user ID of a person whoregistered the content data, and the judgment may be made by using theuser ID.

Embodiment 3

Next, Embodiment 3 will be described below.

FIG. 8 is a view showing the system configuration of an informationrecommendation apparatus 1000 in accordance with this embodiment.

The configuration shown in FIG. 8 is obtained by adding recommendationcondition extraction means 9 to the system configuration of Embodiment 2shown in FIG. 5.

In FIG. 8, numeral 1 designates network interface means, numeral 2designates a content database, numeral 3 designates recommendationcondition input means, numeral 4 designates content recommendationmeans, numeral 5 designates content output means, numeral 6 designatesuser identification means, numeral 7 designates access history controlmeans, and numeral 8 designates content registration means, and numeral9 designates the recommendation condition extraction means of extractingrecommendation conditions from content items and attribute valuesregistered previously by the user.

Furthermore, since a hardware configuration by which the systemconfigured as described above is operated is basically identical to thatof a general-purpose computer system, the explanation of theconfiguration is omitted.

The operation of the information recommendation apparatus operating byusing the system configured as described above will be explained below.In the following description, a dish recommendation system is taken asan example just as in the case of Embodiment 1.

In this Embodiment, first, the user must register content data beforethe user receives the recommendation of information just as in the caseof Embodiment 2.

Since the content data registration procedure in accordance with thisembodiment is similar to that in accordance with Embodiment 2 shown inFIG. 6, its explanation is omitted.

Next, an operation for the user to receive information recommendationwill be described below referring to a flowchart shown in FIG. 9.

(Steps D1 and D2)

When the user issues a request for information recommendation to theinformation recommendation apparatus by using the terminal 100, a checkis made as to whether conditions for having the approval of the user IDand for receiving recommended information have been satisfied or not bythe user identification means 6. In this embodiment, it is essentialthat the user who wishes to receive recommended information mustregister content data beforehand. If the conditions are not satisfied,the processing ends. The operations at steps D1 and D2 are similar tothose at steps C1 and C2 in FIG. 7.

(Step D3)

Next, the recommendation condition extraction means 9 extractsrecommendation conditions from the content data registered previously bythe current user. The access history control means 7 has informationindicating the kind of content data registered in the past by the user.Therefore, to extract the recommendation conditions, the ID of thecontent data registered previously in the content database 2 by the useris read from the access history control means 7, and the content datacorresponding to the data ID is referred to in the content database 2.

Since the content data has a format shown in FIG. 3, the tendencies ofdata registered so far can be extracted depending on various items andattribute values. For example, when attention is paid to the item“ingredients,” the tendencies of ingredients cooked by the user can beknown by obtaining statistics on the ingredients in the content dataregistered by the user, that is, by obtaining the occurrence frequenciesthereof.

The data in a vector form represented by the set of the ingredient namesand the occurrence frequencies obtained as described above is referredto as user characteristic information indicating the tendencies of theuser. In other words, more specifically, the user characteristicinformation is information comprising items, attribute values and theoccurrence frequencies of the attribute values.

FIG. 10 indicates an example of the user tendencies obtained asdescribed above. The names of ingredients are written on the left, andthe values on the right indicate the occurrence frequencies of thenames. Furthermore, FIG. 10 also indicates the user tendencies withrespect to cooking time, calories, etc., as well as the tendencies ofthe ingredients. In addition, the occurrence frequencies are normalized,and the attribute values such as ingredients names and the like arearranged in decreasing order of occurrence frequencies. When explanationis given while attention is paid to ingredients, in the case of thisexample, it can be understood that the user is fond of dishes consistingof pork, onion and cabbage in this order.

For this reason, the names of the ingredients “pork, onion, cabbage, . .. ” are selected as recommendation conditions.

When the current user selects content data registered previously by theuser, in accordance with the data ID of the data registered by the userand recorded in the access history control means 7 described above, datacorresponding to the data ID is referred to. Instead of this method, asshown in FIG. 11, it is possible to use a method wherein a registrant IDis assigned to content data at the time of registration, and data havingthe registrant ID coincident with the user ID of the current user isselected from among content data registered in the content database 2.

(Step D4 to D5)

Recommended information is selected from the content database 2 inaccordance with the recommendation conditions extracted at step D3, andis displayed on the terminal of the user. The procedures for theseoperations are similar to those at steps A2 to A3 in FIG. 2.

(Step D6)

When information is recommended at step D5, the access history controlmeans 7 renews and stores information on the current user, such as thedate when information recommendation is received, the data ID of therecommended information and the number of times the user receivedinformation recommendation.

In this embodiment, when extracting recommendation conditions at stepD3, the extraction is performed in accordance with the occurrencefrequency of each attribute value corresponding to the item“ingredients.” However, the following method may be used. That is, wordsare picked out from the attribute value of the item “cooking method”comprising free sentences. Words regarding cooking methods, such as“broiling” and “boiling,” and words regarding cooking utensils, such as“pan” and “kettle,” are found out. According to the occurrencefrequencies of the words, tendencies of cooking methods conductedfrequently may be found out, whereby recommendation conditions (“broiledfood,” “boiled food,” etc.) based on them may be extracted.

Furthermore, tendencies of cooking time longer or shorter than anaverage and tendencies of calories lower or higher than an average maybe found out from quantitative values indicated by the attribute valuescorresponding to objective items, such as “cooking time” and “calories,”whereby the recommendation conditions based on them may be extracted.

Furthermore, tendencies of frequently having easily cooked dishes orheavy dishes may be found out from the attribute values corresponding tosubjective characteristic amount items, such as “the grade of easiness”and “the grade of heaviness,” whereby the recommendation conditionsbased on them may be extracted.

Furthermore, each piece of data registered in the content database 2 maybe endowed with a content characteristic vector beforehand according tothe occurrence frequencies of keywords, such as ingredients.Furthermore, the inner product of the vector and the user characteristicinformation regarding the user may be obtained from the pastregistration content data of the user as described already, wherebyinformation to be recommended may be determined according to contentinformation having high inner products.

Furthermore, when generating user characteristic information, theinformation of contents recommended to the user and selected by the userin the past may be also included in addition to the contents registeredin the past by the user. Alternatively, the information of the contentsrecommended to the user in the past may also be considered.

Furthermore, the system configuration may be additionally provided witha user characteristic information database 12 wherein the usercharacteristic information of each user is obtained each time the usernewly registers contents and the result is stored. When the usercharacteristic information is required at the time when receivinginformation recommendation, the user characteristic information database12 may be referred to.

These may be combined and the recommendation conditions based on themmay be extracted as a matter of course.

Furthermore, the above-mentioned recommendation conditions automaticallyobtained from the past history of the user may be combined withconditions specifically added by the user.

For example, when the user specifies “beef,” only the contents includingbeef may be selected from the content database 2, and recommendation maybe performed further from among the selected contents by the selectionmethod using the user characteristic information explained already.

With the above-mentioned system configuration and operation, it ispossible to accomplish an information recommendation apparatus whereincontent data stored in the content database 2 can be made complete andappropriate data suited for the user can be recommended while the useris not required to input specific recommendation conditions.

In this embodiment, when recommendation conditions are extracted at stepD3, the tendencies of dishes favorably cooked by the user are extractedaccording to the occurrence frequencies of ingredients and they are usedas recommendation conditions. However, tendencies different from thetendencies of dishes favorably cooked by the user may also be usedintentionally as recommendation conditions. For example, when thetendencies with respect to ingredients shown in FIG. 10 are present,ingredients having low occurrence frequencies are extractedintentionally as recommendation conditions. Alternatively, ingredientshaving no occurrence frequency are extracted as recommendationconditions.

As a result, it is expected that the recipes of dishes not cookedusually by the user be recommended, whereby it is possible to performunexpected information recommendation.

Whether recommendation conditions for reasonable recommendation orrecommendation conditions for unexpected recommendation are extracteddepends on the character of the system. Either of them may be used.

Recommendation viewpoint selection means (not shown) may be added to thesystem configuration of FIG. 8, whereby the system may be configured soas to allow the user to select a viewpoint regarding informationrecommendation.

For example, at the time of requesting information recommendation, theuser himself or herself is allowed to select “reasonable recommendation”or “unexpected recommendation.” Depending on this selection, adetermination is made as to whether recommendation conditions forreasonable recommendation or recommendation conditions for unexpectedrecommendation are extracted. This configuration may also be used.

In this embodiment, user characteristic information and contentcharacteristic information are defined depending on attribute values andthe occurrence frequencies of the attribute values. However, theinformation should only be defined depending on attribute values andconcepts indicating “the weights of the attribute values.” Theoccurrence frequency is an example thereof. As another example, akeyword weight definition method referred to as TF-IDF is available forexample.

Furthermore, it is possible to use an interface using a plane formed oftwo axes as shown in FIG. 18. In this case, the contents registered inthe past by the user are specified according to the user ID input first.According to the specified contents, the average cooking time and thenumber of dishes for the user can be obtained. In other words, thecenter values in FIG. 18 change depending on the user, whereby searchconditions suited for the user can be set.

Similarly, the maximum and minimum values in FIG. 18 can also be setdynamically depending on the contents registered by the user.

For example, it is assumed that a user has carried out contentregistration 10 times so far, and that the average of cooking time is 60minutes, the minimum value is 30 minutes, and the maximum value is 90minutes. In this case, the center of the cooking time axis is set at 60minutes, the left end is set at 30 minutes, and the right end is set at90 minutes.

Furthermore, it is assumed that another user has ever carried outcontent registration 20 times so far, and that the average of cookingtime is 90 minutes, the minimum value is 30 minutes, and the maximumvalue is 150 minutes. The center of the cooking time axis, the left endand the right end are set at the above-mentioned mentioned values,respectively. Similar settings are done for the axis (ordinate) for thenumber of dishes.

Hence, it is possible to set appropriate search ranges suited for theactual situations of each user.

It is explained that the center value of each axis is the average of aregistered content. However, the most frequent value may be used insteadof the average.

The recommendation condition extraction means of this embodiment is anexample of the condition extraction means of the present invention.

Embodiment 4

Next, Embodiment 4 will be described.

FIG. 12 is a view showing the system configuration of an informationrecommendation apparatus in accordance with Embodiment 4.

The configuration shown in FIG. 12 is obtained by adding anadvertisement database 10 to the system configuration of Embodiment 1shown in FIG. 1.

In FIG. 12, numeral 1 designates network interface means, numeral 2designates a content database, numeral 3 designates recommendationcondition input means, numeral 4 designates content recommendationmeans, numeral 5 designates content output means, and numeral 10designates advertisement database for providing advertisement data.

Furthermore, since a hardware configuration by which the systemconfigured as described above is operated is basically identical to thatof a general-purpose computer system, the explanation of theconfiguration is omitted.

The operation of the information recommendation apparatus operating byusing the system configured as described above will be explained below.In the following description, a dish recommendation system is taken asan example just as in the case of Embodiment 1 and explained referringto a flowchart shown in FIG. 13.

(Step E1)

Recommendation conditions input by the user through the terminal 100,that is, items and the attribute values corresponding thereto, aretransmitted via the Internet 500 and received by the informationrecommendation apparatus. This step is the same as step A1 in FIG. 2 inFIG. 2.

(Step E2)

The contents conforming to the recommendation conditions received atstep E1 are selected from the content database 2 by the contentrecommendation means 4. This step is also the same as step A2 in FIG. 2.The method of selecting specific contents is similar to that describedat the step.

(Step E3)

The content recommendation means 4 selects advertisement data conformingto the recommendation conditions input at step E1 from the advertisementdatabase 10. The advertisement data has been registered in theadvertisement database 10 in a format shown in FIG. 14. In FIG. 14,“data ID” designates an inherent number assigned to the advertisementdata, “counter” designates a value indicating the number of times thisadvertisement is transmitted together with recommended information tothe terminal 100, “advertisement” designates the content of theadvertisement, and “related information” designates characteristicamounts characterizing the advertisement. The format of “relatedinformation” is similar to the format of content data in the contentdatabase 2. In other words, just like the format of content data, the“related information” comprises a plurality of items and the attributevalues corresponding to the items. For example, in the “relatedinformation” shown in FIG. 14, “ingredients” is an item, and “beef” isan attribute value corresponding to the item “ingredients.” In addition,“price” is an item, and “low” is an attribute value corresponding to theitem “price.”

Just as in the case of step E2 (step A2), evaluation values based on thedegree of coincidence between recommendation conditions and theattribute values of “Related information” are obtained for all theadvertisement data stored in the advertisement database 10. Theadvertisement data having the highest evaluation value is then selected.

(Step E4)

Regarding the selected advertisement data, the content recommendationmeans 4 increments the value of “counter,” shown in FIG. 14, by one.

(Step E5)

The content (recipe data) selected at step E2 and the advertisement dataselected at step E3 are transmitted to the terminal 100 via the contentoutput means 5 and the network interface 1, and the content and listthereof are displayed on the display screen of the terminal 100. Anexample of such a specific display is shown in FIG. 16. Recipes and anadvertisement 200 are displayed on a WWW browser.

Both text data and image data may be used as the advertisement data for“advertisement” shown in FIG. 14. When the advertisement is text data,the portion of the advertisement 200 in the display example of FIG. 16becomes text. When the advertisement is image data, the portion of theadvertisement 200 becomes the so-called banner advertisement formed ofimages.

In the display example of FIG. 16, the advertisement 200 may be linkedto the home page of its advertiser.

Specific examples of advertisement data are information for providingsample products, information on prizes, information on ingredients,information on cooking utensils, information on restaurants, informationon related retail stores, information on related WEB sites, etc.

As a method of selecting advertisement information, regardless ofrecommendation conditions, selection may be carried out on the basis ofuser characteristic information by comparing the user characteristicinformation with the characteristic amounts of advertisementinformation, that is, the attribute values corresponding to the items inthe “related information.” Alternatively, selection may be carried outin consideration of both the user characteristic information andrecommendation conditions.

With the above-mentioned system configuration and operation, contentdata conforming to the recommendation conditions can be recommended, andan advertisement corresponding to the data can be shown. In addition, itis possible to perform control to find out which advertisement isdisplayed and how many times the advertisement is displayed. Hence, itis possible to charge the advertiser of the advertisement anadvertisement rate depending on the number of times.

In the above-mentioned Embodiment 1 to Embodiment 4, recipes for dishesare exemplified as content data. However, the present invention is notlimited to this, but can be applied to various contents.

In the above-mentioned Embodiment 1 to Embodiment 4, the request ofinformation recommendation, the input of recommendation conditions, theregistration of new content data, the display of recommended contentdata, etc. are explained by taking examples using a WWW browser.However, the present invention is not limited to this, but may beconfigured so that another means, such as electronic mail, may be usedto transmit such information between the terminal 100 and theinformation recommendation apparatus 1000.

The content recommendation means 4 of this embodiment is used as anexample of the advertisement specifying means of the present invention.The counter of this embodiment is used as an example of theadvertisement counter of the present invention.

Embodiment 5

Next, Embodiment 5 will be described.

FIG. 19 is a view showing the system configuration of an informationrecommendation apparatus in accordance with Embodiment 5.

The configuration shown in FIG. 19 is obtained by adding similar userselection means 11 to the system configuration of Embodiment 2 shown inFIG. 5.

In FIG. 19, numeral 1 designates network interface means, numeral 2designates a content database, numeral 3 designates recommendationcondition input means, numeral 4 designates content recommendationmeans, numeral 5 designates content output means, numeral 6 designatesuser identification means, numeral 7 designates access history controlmeans, numeral 8 designates content registration means, numeral 13designates user characteristic information calculation means ofobtaining user characteristic information indicating the tendencies ofthe content data input in the past by the user by calculation, numeral12 designates a user characteristic information database for storingvectors obtained by the user characteristic information obtaining means13, and numeral 11 designates the similar user selection means 11 ofselecting other users similar to the user attempting to receiveinformation recommendation.

Furthermore, since a hardware configuration by which the systemconfigured as described above is operated is basically identical to thatof a general-purpose computer system, the explanation of theconfiguration is omitted.

The operation of the information recommendation apparatus operating byusing the system configured as described above will be explained below.In the following description, a dish recommendation system is taken asan example just as in the case of Embodiment 1.

In this Embodiment, first, the user must register content data beforethe user receives the recommendation of information just as in the caseof Embodiment 2.

The content data registration procedure in accordance with thisembodiment is similar to that in accordance with Embodiment 2 shown inFIG. 6.

FIG. 20 shows the procedure. In this procedure, new step B4-2 is addedbetween step B4 and step B5 in FIG. 6.

In other words, contents are registered in accordance with a proceduresimilar to that shown in FIG. 6. User access history is renewed at stepB4. At step B4-2, by referring to the content data registered by theuser so far from the content database 2, the user characteristicinformation is renewed and registered in the user characteristicinformation database 12.

The method of specifically generating and renewing the usercharacteristic information is the same as that explained in Embodiment3. For example, only the data registered by the user is selected fromamong the data registered in the content database 2. The names of theingredients occurred in the registered data and the frequencies of theoccurrences are counted, and the frequencies are normalized. As aresult, the format shown in FIG. 10 is obtained.

The user characteristic information additionally including newlyregistered content data is renewed by a similar procedure.

By the above-mentioned procedure, the content data is registered, andthe user characteristic information is also generated and renewedsimultaneously.

The generation and renewal of the user characteristic information arenot necessarily required to be carried out at the time of theregistration of the content data. When the content data is registered,the registration may be carried out according to the procedure shown inFIG. 6, and when the load on the information recommendation system isrelatively low, the renewal operation of the user characteristicinformation, that is, only the operation at step B4-2 in FIG. 20, may becarried out.

Next, an operation for the user to receive information recommendationwill be described below referring to a flowchart shown in FIG. 21.

(Steps F1 and F2)

When the user issues a request for information recommendation to theinformation recommendation apparatus by using the terminal 100, a checkis made as to whether conditions for having approval of the user ID andfor receiving recommended information have been satisfied or not. Inthis embodiment, it is essential that the user who wishes to receiverecommended information must register content data beforehand. If theconditions are not satisfied, the processing ends.

If the conditions for receiving information recommendation are satisfiedat step F2, the recommendation conditions are accepted from the user.This operation is similar to that at steps C1 to C3 in FIG. 7.

(Step F4)

Next, users similar to the current user are selected as described below.By referring to the user characteristic information database 12 via theuser ID, it is possible to refer to the user characteristic informationof the user. This user characteristic information has a format shown inFIG. 10 as explained already.

The similar user selection means 11 compares the user characteristicinformation of the user with the user characteristic information ofother users in the user characteristic information database 12 andselects similar users. As an example of a specific selection method, theinner products of the vector of the user and the vectors of other usersare obtained, and the vectors of users having high inner products areselected. By this method, at least one or more other users are selected.

(Step F5)

Among all content data in the content database, the data registered bythe users selected at step F4 is selected depending on therecommendation conditions accepted at step F3.

As a specific recommendation data selection method, a procedure similarto that used at steps A21 to A24 shown in FIG. 4 and described alreadyin the explanation of Embodiment 1 can be used. Alternatively, asdescribed already in the explanation of Embodiment 3, content data maybe endowed with content characteristic vectors, and a determination maybe made by comparing the user characteristic information of the user whorequested recommendation with the content characteristic vectors.

(Steps F6 and F7)

The recommended information determined at step F5 is displayed on theterminal of the user. The access history control means 7 renews andstores information on the current user, such as the date wheninformation recommendation is received, the data ID of the recommendedinformation and the number of times the user received informationrecommendation.

In this embodiment, the following selection method may be used. Wheninformation to be recommended is determined at step F5, a confirmationis made as to whether the user attempting to receive recommendation hasever received the information to be recommended and selected theinformation or not, by referring to the content database 2, whereby onlythe unselected content data can be selected.

At the time of inputting recommendation conditions, the interface shownin FIG. 17 or FIG. 18 may be used as described already in theexplanation of Embodiment 2.

Furthermore, at the time of generating user characteristic information,in addition to using contents registered by the user in the past, it maybe possible to consider content data information recommended to the userand selected by the user in the past. In other words, each time the userreceived the recommendation of content data and selected the recommendedcontent data in the past, the user characteristic information of theuser may have been obtained by calculation. Alternatively, it may bepossible to consider content data information recommended to the user inthe past. In other words, each time the user received contentrecommendation in the past, the user characteristic information of theuser may have been obtained by calculation.

Furthermore, the following method may also be used. While no specificrecommendation condition is input by the current user, only the user IDis accepted, other users having user characteristic information similarto that of the current user are selected. Even when the user attemptingto receive recommendation simply selects and indicates recommended dataor data that was recommended to the user but not selected by the userfrom the contents registered by the selected other users, the contentsare well worth recommendation, because the contents are those registeredby the other users having similar preferences. Hence, this simplifiedmethod may also be used.

With the above-mentioned system configuration and operation, it ispossible to accomplish an information recommendation apparatus whereincontent data stored in the content database 2 can be made complete andcontent data registered by users having similar preferences can berecommended.

The user characteristic information calculation means 13 of thisembodiment is an example of characteristic calculation means of thepresent invention, and the user characteristic information of thisembodiment is an example of a user characteristic of each item in thepresent invention.

Embodiment 6

Next, Embodiment 6 will be described below.

FIG. 22 is a view showing the system configuration of an informationrecommendation apparatus in accordance with Embodiment 6. Thisconfiguration is obtained by adding content registration means 8 to thesystem configuration of Embodiment 1.

The format of the data registered in the content database 2 is similarto that shown in FIG. 3.

Although Embodiment 6 is similar to Embodiment 1 as described above,Embodiment 6 can have new effects by changing the recommendationcondition acceptance method and the search method of Embodiment 1. Theoperations in this embodiment will be described below.

Furthermore, since a hardware configuration by which the systemconfigured as described above is operated is basically identical to thatof a general-purpose computer system, the explanation of theconfiguration is omitted.

In the following description, a dish recommendation system is taken asan example and described referring to a flowchart shown in FIG. 23.

(Preparation)

Abundant pieces of content data have been registered beforehand in thecontent database 2. By forming a configuration wherein many users canregister content data through the terminal 100 by using the contentregistration means 8, abundant various content data can be collectedeasily.

(Step G1)

Recommendation conditions input by the user through the terminal 100 aretransmitted via the Internet 500 and received by the network interfacemeans 1 of the information recommendation apparatus 1000.

For example, when “curry” is input as a recommendation condition throughthe terminal 100, that is, when “recipe” is input as an item and “curry”is input as an attribute value, these are transmitted to the informationrecommendation apparatus 1000 and input to the recommendation conditioninput means 3.

(Step G2)

According to the recommendation condition received at step G1, thecontent recommendation means 4 selects data in which recipe names usedas attribute values corresponding to the item “recipe” in the contentdatabase 2 include the recommendation condition.

For example, when the attribute values corresponding to the item“recipe” are “curry and rice,” “curry and spaghetti,” “seafood curry,”“curry doria,” etc., these recipe names include the character string“curry” used as the recommendation condition. These contents areselected. As described above, even the contents having attribute valuesincluding content data partially coincident with the recommendationcondition can become objects to be selected.

(Step G3)

The contents (recipe data) selected at step G2 are transmitted from thecontent output means 5 to the terminal 100 via the network interfacemeans 1, and the contents and lists thereof are displayed on the displayscreen of the terminal 100.

The display method of the terminal 100 is not specified in particular.However, recipes are displayed on a WWW browser as shown in FIG. 15, forexample.

At this time, two or more contents having the same recipe name maypresent. For example, two or more contents having the recipe name “curryand rice” may present. In this case, only one of the contents isselected as “curry and rice” to be output to the terminal on the basisof a predetermined standard, and the rest is discarded.

By using the procedure described above, contents obtained by applyingthe recommendation conditions or by deriving the recommendationconditions can be recommended from among all of content data in thecontent database 2.

In the case of recipes for dishes in particular, it is supposed thatinfinite recipes are available. That is, original recipes created forevery family or every individual are available. In this embodiment,since the user can register contents, such various recipes can becollected. Furthermore, by using the collected recipes, various “curry”application recipes can be recommended in the case of the condition“curry.”

Embodiment 7

Next, Embodiment 7 will be described below.

FIG. 22 is a view showing the system configuration of an informationrecommendation apparatus in accordance with Embodiment 7. This systemconfiguration is the same as that of Embodiment 6.

The format of the data registered in the content database 2 is shown inFIG. 24. In FIG. 24, all items of the recipes served for a meal aredescribed in one piece of content data, and “data ID,” “recipe name,”“cooking method,” “ingredients,” etc. are available as attributes. Amongthese, it is essential that the names of all dishes taken at a mealshould be written in the “recipe name.” The other items “cookingmethod,” “ingredients,” etc. are not essential. In addition, items otherthan the attributes indicated in the figure may be included.

As described above, Embodiment 7 can have new effects by changing theformat of content data registered in the content database 2, by changingthe recommendation condition acceptance method and by changing thesearch method. The operations in this embodiment will be describedbelow.

Furthermore, since a hardware configuration by which the systemconfigured as described above is operated is basically identical to thatof a general-purpose computer system, the explanation of theconfiguration is omitted.

In the following description, a dish recommendation system is taken asan example and described referring to a flowchart shown in FIG. 25.

(Preparation)

Abundant pieces of content data have been registered beforehand in thecontent database 2. By forming a configuration wherein many users canregister content data through the terminal 100 by using the contentregistration means 8, abundant various content data can be collectedeasily.

(Step H1)

Recommendation conditions input by the user through the terminal 100 aretransmitted via the Internet 500 and received by the network interfacemeans 1 of the information recommendation apparatus 1000.

For example, when “hamburger” is input as a recommendation conditionthrough the terminal 100, that is, when “recipe name” is input as anitem and “hamburger” is input as an attribute value correspondingthereto, these are transmitted to the information recommendationapparatus 1000, and input to the recommendation condition input means 3.

(Step H2)

According to the recommendation condition received at step H1, thecontent recommendation means 4 selects data in which one of recipe namesin the content database 2 includes the recommendation condition.

For example, when content data includes a group of recipe names “curryand rice,” “fruit salad,” and “oolong tea,” the recommendation condition“hamburger” is not included therein. Hence, the content data is notselected.

Furthermore, when content data includes a group of recipe names“hamburger,” “Caesar salad” and “potage soup,” the recommendationcondition “hamburger” is included therein. Hence, the content data isselected.

(Step H3)

Next, recipe names different from the recommendation condition areselected from the group of recipe names in the content data selected atstep H2. For example, when the group consists of “hamburger,” “Caesarsalad” and “potage soup,” “Caesar salad” or “potage soup” different fromthe recommendation condition “hamburger” is selected. In this case, oneof them or both of them may be selected. Alternatively, all the recipenames including the recipe name used as the recommendation condition maybe selected from the same group of recipe names.

(Step H4)

The recipe names selected at step H3 are transmitted from the contentoutput means 5 to the terminal 100 via the network interface means 1,and the contents and lists thereof are displayed on the display screenof the terminal 100.

The display method of the terminal 100 is not specified in particular.However, recipes are displayed on a WWW browser as shown in FIG. 15, forexample.

At this time, when two or more contents having the same recipe name arepresent, only one of the contents is output to the terminal, and therest is discarded.

By using the procedure described above, content data suited to beprovided together with certain content data can be recommended fromamong all of content data in the content database 2.

Furthermore, the recipe names of dishes taken for a meal are describedin a piece of content data registered in the content database 2.However, there is no setting of a main-and-subordinate relation amongthem. The recipe names of dishes taken simultaneously are simplydescribed. Therefore, various recipes can be recommended without beingrestricted by the concept of main and subordinate dishes.

In this embodiment, the recipe names of dishes taken for a meal aredescribed in a piece of content data. However, this embodiment is notlimited to this. Identification information indicating dishes takensimultaneously may be assigned to all of the content data regarding thedishes taken simultaneously. Alternatively, a table indicating therelationship among the content data regarding dishes takensimultaneously may be prepared separately. Alternatively, a common IDmay be assigned to the content data regarding dishes takensimultaneously.

The content data in which all the recipes for dishes taken for a meal inaccordance with this embodiment is an example of a recipe group ofdishes taken for a meal. A piece of data corresponding to each recipe inaccordance with this embodiment is an example of a recipe in accordancewith the present invention. Furthermore, when identification informationindicating dishes taken simultaneously is assigned to all of the contentdata regarding the dishes taken simultaneously, the content dataregarding the dishes taken simultaneously is an example of a recipegroup of dishes taken for a meal. In this case, the content data is anexample of a recipe in accordance with the present invention.Furthermore, when a table indicating the relationship among content dataregarding dishes taken simultaneously is prepared separately, thecontent data related by the table is an example of a recipe group fordishes taken for a meal. In this case, the content data is an example ofa recipe in accordance with the present invention.

Embodiment 8

Next, Embodiment 8 will be described below.

FIG. 26 is a view showing the system configuration of an informationrecommendation apparatus in accordance with Embodiment 8. Thisconfiguration is obtained by adding type information calculation means15 of obtaining the registered user type information by calculation fromthe data registered in the content database 2, by adding a typeinformation database 16 in which the user type information obtained bycalculation by the type information calculation means 15, and by addingtype information selection means 14 of selecting type informationsimilar to information on the user identified by the user identificationmeans 6 from the type information database 16 to the configuration ofEmbodiment 2. The configuration of Embodiment 2 shown in FIG. 5comprises the network interface means 1, the content database 2, therecommendation condition input means 3, the content recommendation means4, the content output means 5, the user identification means 6, theaccess history control means 7 and the content registration means 8.

Furthermore, since a hardware configuration by which the systemconfigured as described above is operated is basically identical to thatof a general-purpose computer system, the explanation of theconfiguration is omitted.

The type information is statistical information on users conforming to acertain condition. For example, it is assumed that 1000 users registeredinformation in the content database 2. Among the users, it is assumedthat 500 users live in the Kanto area, that 400 users live in the Kansaiarea, and that 100 users, i.e., the rest of the users, live in otherareas. When obtaining “type information in the Kanto area” for example,a procedure to be used at this time is similar to the procedure forgenerating the user characteristic information described in theexplanation of Embodiment 3. For example, only the data registered bythe users living in the Kanto area is selected from among the dataregistered in the content database 2. The ingredient names occurring inthe registered data and their occurrence frequencies are counted, andthe frequencies are normalized as shown in FIG. 10. The obtainedinformation indicates tendencies regarding food for the users living inthe Kanto area. This information is referred to as type information.

This kind of type information can be obtained by calculation fromvarious viewpoints, such as the distinction of sex, age bracket,occupation and the distinction between unmarried and married, inaddition to the type information depending on the user classified byresidential area.

In this embodiment, the user is first required to register content databefore the user receives information recommendation just as in the caseof Embodiment 2.

The procedure for registering the content data is similar to the contentdata registration procedure shown in FIG. 6 and described in theexplanation of Embodiment 2.

FIG. 27 shows the procedure. In this procedure, new step B4-3 is addedbetween step B4 and step B5 in FIG. 6.

In other words, contents are registered in accordance with a proceduresimilar to that shown in FIG. 6. User access history is renewed at stepB4. At step B4-3, by referring to the content data registered by theuser so far from the content database 2, the type informationcalculation means 15 renews the user type information and registers theinformation in the type information database 16.

The type information calculation method is as described before. The typeinformation additionally including currently registered information isrenewed.

By the above-mentioned procedure, the content data is registered, andthe type information is also generated and renewed simultaneously.

The generation and renewal of the type information are not necessarilyrequired to be carried out at the time of the registration of thecontent data. When the content data is registered, the registration maybe carried out according to the procedure shown in FIG. 6, and when theload on the information recommendation system is relatively low, therenewal operation of the type information, that is, only the operationat step B4-3 in FIG. 27, may be carried out.

Next, an operation for the user to receive information recommendationwill be described below referring to a flowchart shown in FIG. 28.

(Steps I1 to I3)

When the user issues a request for information recommendation to theinformation recommendation apparatus by using the terminal 100, a checkis made as to whether conditions for having approval of the user ID andfor receiving recommended information have been satisfied or not. Inthis embodiment, it is essential that the user who wishes to receiverecommended information must register content data beforehand. If theconditions are not satisfied, the processing ends.

If the conditions for receiving information recommendation are satisfiedat step I2, the recommendation conditions are accepted from the user.This operation is similar to that at steps C1 to C3 in FIG. 7.

(Step I4)

The contents registered in the content database 2 are selected on therecommendation conditions accepted at step I3. As a specific recommendedcontent selection method, a procedure similar to that used at steps A21to A24 shown in FIG. 4 and described already in the explanation ofEmbodiment 1 can be used.

Alternatively, as described already in the explanation of Embodiment 3,content data may also be endowed with content characteristic vectors,and a determination may be made by comparing the user characteristicinformation of the user who requested recommendation with contentcharacteristic vectors. The user characteristic information may beobtained dynamically from the content data that is registered by theuser in the past in the content database 2. In addition, by using theconfiguration having the user characteristic information database 12(not shown), the characteristic vector of the user, registered in theuser characteristic information database 12, may be referred to.

(Step I5)

Next, the type corresponding to the current user is selected. In thisselection, the type best conforming to the user is selected from thetype information database 16. As an example of a specific selectionmethod, the inner products of user characteristic information and typedata written in vector are obtained, and the largest inner product isselected.

For example, information best conforming to the type information on“company employee living in the Kansai area” is selected from theinformation on all the registered users.

(Steps I6 and I7)

The recommended information determined at step I4 and the typeinformation determined at step I5 are displayed on the terminal of theuser. The access history control means 7 renews and stores informationfor the current user, such as the date when information recommendationis received, the data ID of the recommended information and the numberof times the user received information recommendation.

FIG. 29 is a display example of a result of information recommendationat the terminal.

This indicates that the user corresponds to the type information on“company employee living in the Kansai area.”

The type information selection means 14 of this embodiment is an exampleof type judgment means of the present invention.

With the above-mentioned system configuration and operation, it ispossible to indicate the type of the user together with recommendedcontent information. In comparison with the indication of onlyrecommended contents, the indication of the type information togetherwith recommended contents provides the preferences and selectioncharacteristics of the user by using other ways of expression. Hence,the user can find his or her unintentional tendencies. As a result, theinformation recommendation system can be made more interesting andconvenient.

In Embodiments 1 to 8, an example using the Internet is described ascommunication means of connecting the server apparatus to the terminals.However, instead of the Internet, public telephone networks, portabletelephone networks and digital broadcasting networks using satellitesand ground waves may also be used. Alternatively, it may be possible touse an asymmetrical configuration of communication means wherein digitalbroadcasting networks are used from the server apparatus to theterminals, and the Internet is used from the terminals to the serverapparatus.

As described above, in Embodiment 1, contents and attribute values havebeen registered in the content database 2. Hence, it is possible toaccomplish an information recommendation apparatus capable ofrecommending data conforming to the request of the user from amongcontent data having been stored in the content database 2. By providingcontent data having subjective characteristic amounts as attributes inparticular, information recommendation can be attained according tosubjective or sensitive recommendation conditions, such as “easy-cookingdish,” “light dish” and “enjoyable dish.”

Furthermore, in Embodiment 2, information recommendation is limiteddepending on the registration results of contents. Hence, it is possibleto accomplish an information recommendation apparatus wherein contentdata stored in the content database 2 can be made complete and dataconforming to the request of the user can be recommended.

Furthermore, the user is asked to enter subjective evaluation items atthe time of content registration. Hence, the relative merits of theregistered contents can be determined. By considering this matter at thetime of content recommendation, contents having higher merits can berecommended.

Furthermore, the user is asked to enter items regarding cause-effectrelations before and after eating at the time of content registration.Hence, the user can designate conditions in view of cause-effectrelations of a meal, such as “a dish to be taken when not feeling well,”“a dish giving pleasant feelings after eating,” and the user can receivethe recommendation of contents.

Furthermore, the user is asked to enter items regarding the person whoprepares a dish or the person who eats a dish at the time ofregistration. Hence, the user can designate conditions from thestandpoint of the person who prepares a dish or the person who eats adish, and can receive the recommendation of contents.

Moreover, the user can search for contents easily with an interfacecapable of making input easy by using registration items that can bemade quantitative.

Furthermore, in Embodiment 3, information recommendation is limiteddepending on the registration results of contents, and recommendationconditions are extracted from the contents registered by the user.Hence, it is possible to accomplish an information recommendationapparatus wherein appropriate data suited for the user can berecommended while the user is not required to input specificrecommendation conditions.

Furthermore, in Embodiment 4, content data conforming to therecommendation conditions can be recommended, and an advertisementcorresponding to the data can be shown. In addition, it is possible toperform control to find out which advertisement is displayed and howmany times the advertisement is displayed. Hence, it is possible tocharge the advertiser of the advertisement an advertisement ratedepending on the number of times.

Furthermore, in Embodiment 5, it is possible to accomplish aninformation recommendation apparatus wherein content data stored in thecontent database 2 can be made complete, users having similarpreferences can be selected, and content data registered by the usershaving can be recommended.

Furthermore, in Embodiment 6, it is possible to accomplish aninformation recommendation apparatus wherein contents obtained byapplying the recommendation conditions or by deriving from therecommendation conditions can be recommended from among all of contentdata in the content database 2.

Furthermore, in Embodiment 7, it is possible to accomplish aninformation recommendation apparatus wherein content data suited to beprovided together with certain content data can be recommended fromamong all of content data in the content database 2.

Furthermore, in Embodiment 8, it is possible to indicate the type of theuser together with recommended content information. In comparison withthe indication of only recommended contents, the indication of the typeinformation together with recommended contents provides the preferencesand selection characteristics of the user by using other ways ofexpression. Hence, the user can find his or her unintentionaltendencies. As a result, it is possible to accomplish an informationrecommendation apparatus being more interesting and convenient.

The program of the present invention operates together with a computerso that the computer carries out the functions of all or some means(apparatuses, devices, circuits, etc.) of the above-mentionedinformation recommendation apparatus of the present invention.

Some means (apparatuses, devices, circuits, etc.) of the presentinvention are defined as several means in a plurality of means or somefunctions in one means.

Furthermore, the present invention includes recording media on which theprogram of the present invention is recorded and which can be read bycomputers.

Furthermore, a usage configuration of the program of the presentinvention may be an embodiment wherein the program is recorded on therecording media capable of being read by computers and the programoperates together with computers.

Furthermore, another usage configuration of the program of the presentinvention may be an embodiment wherein the program is transmittedthrough transmission media, read by computers and executed together withcomputers.

Furthermore, ROM and the like are included as recording media. TheInternet, light/electric waves, sound waves and the like are included astransmission media.

Furthermore, the above-mentioned computers of the present invention arenot limited to pure hardware, such as CPU, but may include firmware, OSand peripherals.

As described above, the configuration of the present invention may beaccomplished by software or by hardware.

EFFECT OF THE INVENTION

As described above clearly, in order to accomplish information serviceusing an information server, the present invention can provide aninformation recommendation apparatus, an information recommendationsystem and a program capable of preparing abundant contents.

Furthermore, the present invention can provide an informationrecommendation apparatus, an information recommendation system and aprogram capable of recovering maintenance cost for constructing andmaintaining a large database.

Furthermore, the present invention can provide an informationrecommendation apparatus, an information recommendation system and aprogram capable of easily finding out information that is exactly suitedfor a user but unnoticed.

Furthermore, the present invention can provide an informationrecommendation apparatus, an information recommendation system and aprogram capable of providing recipes suited for actual daily menus forfamily.

Furthermore, the present invention can provide an informationrecommendation apparatus, an information recommendation system and aprogram capable of providing recipes not void of viewpoints obtained bythe result of actually using the recipes and suited for actualsituations.

1. An information recommendation apparatus comprising: recommendationmeans of selecting and recommending contents coincident with or similarto conditions input by condition input means of inputting saidconditions represented by predetermined items and attribute valuescorresponding thereto, from among contents formed of plural pieces ofdata having plural items and attribute values corresponding thereto andstored in a content database in which said contents are registered byregistration means, wherein said recommended contents are output byoutput means, and said conditions input to said condition input meansare conditions extracted by condition extraction means of automaticallyextracting said conditions.
 2. An information recommendation apparatusaccording to claim 1, wherein the conditions to be input to saidcondition input means are those extracted on the basis of contentsregistered in the past by a user who will receive recommendation.
 3. Aninformation recommendation apparatus according to claim 1, wherein thecharacteristic of each item of said user is obtained by calculation eachtime said user registers said data.
 4. An information recommendationapparatus according to claim 1, wherein the conditions to be input tosaid condition input means are extracted on the basis of contentsrecommended in the past to a user who is attempting to receiverecommendation or on the basis of contents recommended to and specifiedby said user.
 5. An information recommendation apparatus according toclaim 4, wherein the characteristic of each item of said user isobtained by calculation by characteristic calculation means each timesaid user receives recommendation or each time said user receivesrecommendation and specifies the contents.
 6. An informationrecommendation apparatus according to claim 2, wherein, when saidconditions are extracted from said contents, said conditions havingtendencies opposite to those of said contents are extracted.
 7. Aninformation recommendation apparatus according to claim 1, wherein saidcondition input means inputs said externally input conditions and saidautomatically extracted conditions, and said recommendation meansselects contents coincident with or similar to said automaticallyextracted conditions from only said contents conforming to saidexternally input conditions and recommend said selected contents.
 8. Aninformation recommendation apparatus according to claim 4, wherein, whensaid conditions are extracted from said contents, said conditions havingtendencies opposite to those of said contents are extracted.